Kim, Minkyung; Crossley, Scott A.; Kyle, Kristopher
This study conceptualizes lexical sophistication as a multidimensional phenomenon by reducing numerous lexical features of lexical sophistication into 12 aggregated components (i.e., dimensions) via a principal component analysis approach. These components were then used to predict second language (L2) writing proficiency levels, holistic lexical…
Sodiya, Adesina Simon
Natural languages are the latest generation of programming languages, which require processing real human natural expressions. Over the years, several groups or researchers have trying to develop widely accepted natural language languages based on artificial intelligence (AI). But no true natural language has been developed. The goal of this work is to design a natural language preprocessing architecture that identifies and accepts programming instructions or sentences in their natural forms ...
Sharp, J.K. [Sandia National Labs., Albuquerque, NM (United States)
This seminar describes a process and methodology that uses structured natural language to enable the construction of precise information requirements directly from users, experts, and managers. The main focus of this natural language approach is to create the precise information requirements and to do it in such a way that the business and technical experts are fully accountable for the results. These requirements can then be implemented using appropriate tools and technology. This requirement set is also a universal learning tool because it has all of the knowledge that is needed to understand a particular process (e.g., expense vouchers, project management, budget reviews, tax, laws, machine function).
Laporte , Eric
The connection between language processing and combinatorics on words is natural. Historically, linguists actually played a part in the beginning of the construction of theoretical combinatorics on words. Some of the terms in current use originate from linguistics: word, prefix, suffix, grammar, syntactic monoid... However, interpenetration between the formal world of computer theory and the intuitive world of linguistics is still a love story with ups and downs. We will encounter in this cha...
A sophisticated player is an individual who takes the action of the opponents, in a strategic situation, as determined by decision of rational opponents, and acts accordingly. A sophisticated agent is rational in the choice of his action, but ignores the fact that he is part of a strategic
Discusses the thinking of the Greek Sophist philosophers, particularly Gorgias and Protagoras, and their importance and relevance for contemporary English instructors. Considers the problem of language as signs of reality in the context of Sophist philosophy. (HB)
Language understanding is essential for intelligent information processing. Processing of language itself involves configuration element analysis, syntactic analysis (parsing), and semantic analysis. They are not carried out in isolation. These are described for the Japanese language and their usage in understanding-systems is examined. 30 references.
Corneli, Joseph; Corneli, Miriam
"Natural Language," whether spoken and attended to by humans, or processed and generated by computers, requires networked structures that reflect creative processes in semantic, syntactic, phonetic, linguistic, social, emotional, and cultural modules. Being able to produce novel and useful behavior following repeated practice gets to the root of both artificial intelligence and human language. This paper investigates the modalities involved in language-like applications that computers -- and ...
Provides a comprehensive, modern reference of practical tools and techniques for implementing natural language processing in computer systems. This title covers classical methods, empirical and statistical techniques, and various applications. It describes how the techniques can be applied to European and Asian languages as well as English
Hirschberg, Julia; Manning, Christopher D
Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area. Copyright © 2015, American Association for the Advancement of Science.
Reese, Richard M
If you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.
Hoard, James E.
Integrating diverse information sources and application software in a principled and general manner will require a very capable advanced information management (AIM) system. In particular, such a system will need a comprehensive addressing scheme to locate the material in its docuverse. It will also need a natural language processing (NLP) system of great sophistication. It seems that the NLP system must serve three functions. First, it provides an natural language interface (NLI) for the users. Second, it serves as the core component that understands and makes use of the real-world interpretations (RWIs) contained in the docuverse. Third, it enables the reasoning specialists (RSs) to arrive at conclusions that can be transformed into procedures that will satisfy the users' requests. The best candidate for an intelligent agent that can satisfactorily make use of RSs and transform documents (TDs) appears to be an object oriented data base (OODB). OODBs have, apparently, an inherent capacity to use the large numbers of RSs and TDs that will be required by an AIM system and an inherent capacity to use them in an effective way.
Krahmer, Emiel; Theune, Mariet
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field
Ngo, Thu Thi Bich
Evaluation is an important aspect in communication in any language as it not only functions to express language users' evaluative stance but also to construct and maintain relations between interactants. In the teaching of languages in addition to English, paying attention to evaluative language contributes to an understanding of the…
Nadkarni, Prakash M; Ohno-Machado, Lucila; Chapman, Wendy W
To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation's Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.
Hassani, Kaveh; Lee, Won-Sook
A natural language interface exploits the conceptual simplicity and naturalness of the language to create a high-level user-friendly communication channel between humans and machines. One of the promising applications of such interfaces is generating visual interpretations of semantic content of a given natural language that can be then visualized either as a static scene or a dynamic animation. This survey discusses requirements and challenges of developing such systems and reports 26 graphi...
Natural language processing techniques for automatic test questions generation using discourse connectives. ... PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ... Journal of Computer Science and Its Application.
In principle, natural language and knowledge representation are closely related. This paper investigates this by demonstrating how several natural language phenomena, such as definite reference, ambiguity, ellipsis, ill-formed input, figures of speech, and vagueness, require diverse knowledge sources and reasoning. The breadth of kinds of knowledge needed to represent morphology, syntax, semantics, and pragmatics is surveyed. Furthermore, several current issues in knowledge representation, such as logic versus semantic nets, general-purpose versus special-purpose reasoners, adequacy of first-order logic, wait-and-see strategies, and default reasoning, are illustrated in terms of their relation to natural language processing and how natural language impact the issues.
Mobile Speech and Advanced Natural Language Solutions provides a comprehensive and forward-looking treatment of natural speech in the mobile environment. This fourteen-chapter anthology brings together lead scientists from Apple, Google, IBM, AT&T, Yahoo! Research and other companies, along with academicians, technology developers and market analysts. They analyze the growing markets for mobile speech, new methodological approaches to the study of natural language, empirical research findings on natural language and mobility, and future trends in mobile speech. Mobile Speech opens with a challenge to the industry to broaden the discussion about speech in mobile environments beyond the smartphone, to consider natural language applications across different domains. Among the new natural language methods introduced in this book are Sequence Package Analysis, which locates and extracts valuable opinion-related data buried in online postings; microintonation as a way to make TTS truly human-like; and se...
Hovy, Eduard H
Recognizing that the generation of natural language is a goal- driven process, where many of the goals are pragmatic (i.e., interpersonal and situational) in nature, this book provides an overview of the role of pragmatics in language generation. Each chapter states a problem that arises in generation, develops a pragmatics-based solution, and then describes how the solution is implemented in PAULINE, a language generator that can produce numerous versions of a single underlying message, depending on its setting.
facilities. BBN is developing a series of increasingly sophisticated natural language understanding systems which will serve as an integrated interface...Haas, A.R. A Syntactic Theory of Belief and Action. Artificial Intelligence. 1986. Forthcoming.  Hinrichs, E. Temporale Anaphora im Englischen
Levison, Michael; Lessard, Gregory
Describes the natural language computer program, "Vinci." Explains that using an attribute grammar formalism, Vinci can simulate components of several current linguistic theories. Considers the design of the system and its applications in linguistic modelling and second language acquisition research. Notes Vinci's uses in linguistics…
Sevens, Leen; Vandeghinste, Vincent; Schuurman, Ineke; Van Eynde, Frank
We present a Pictograph-to-Text translation system for people with Intellectual or Developmental Disabilities (IDD). The system translates pictograph messages, consisting of one or more pictographs, into Dutch text using WordNet links and an n-gram language model. We also provide several pictograph input methods assisting the users in selecting the appropriate pictographs.
This dissertation studies how people describe emotions with language and how computers can simulate this descriptive behavior. Although many non-human animals can express their current emotions as social signals, only humans can communicate about emotions symbolically. This symbolic communication of emotion allows us to talk about emotions that we…
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.
Adelphi, MD 20783-1197 This technical note provides a brief description of a Java library for Arabic natural language processing ( NLP ) containing code...for training and applying the Arabic NLP system described in the paper "A Cross-Task Flexible Transition Model for Arabic Tokenization, Affix...and also English) natural language processing ( NLP ), containing code for training and applying the Arabic NLP system described in Stephen Tratz’s
Berwick, Robert C; Friederici, Angela D; Chomsky, Noam; Bolhuis, Johan J
Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human. Copyright © 2012 Elsevier Ltd. All rights reserved.
Monti, Martin M; Parsons, Lawrence M; Osherson, Daniel N
A central question in cognitive science is whether natural language provides combinatorial operations that are essential to diverse domains of thought. In the study reported here, we addressed this issue by examining the role of linguistic mechanisms in forging the hierarchical structures of algebra. In a 3-T functional MRI experiment, we showed that processing of the syntax-like operations of algebra does not rely on the neural mechanisms of natural language. Our findings indicate that processing the syntax of language elicits the known substrate of linguistic competence, whereas algebraic operations recruit bilateral parietal brain regions previously implicated in the representation of magnitude. This double dissociation argues against the view that language provides the structure of thought across all cognitive domains.
Andreasen, Troels; Styltsvig, Henrik Bulskov; Jensen, Per Anker
We describe a natural logic for computational reasoning with a regimented fragment of natural language. The natural logic comes with intuitive inference rules enabling deductions and with an internal graph representation facilitating conceptual path finding between pairs of terms as an approach t...
Andreasen, Troels; Bulskov, Henrik; Jensen, Per Anker
We describe a natural logic for computational reasoning with a regimented fragment of natural language. The natural logic comes with intuitive inference rules enabling deductions and with an internal graph representation facilitating conceptual path finding between pairs of terms as an approach t...
Willems, Roel M; Frank, Stefan L; Nijhof, Annabel D; Hagoort, Peter; van den Bosch, Antal
The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as well as surprisal A computational model determined entropy and surprisal for each word in 3 literary stories. Twenty-four healthy participants listened to the same 3 stories while their brain activation was measured using fMRI. Reversed speech fragments were presented as a control condition. Brain areas sensitive to entropy were left ventral premotor cortex, left middle frontal gyrus, right inferior frontal gyrus, left inferior parietal lobule, and left supplementary motor area. Areas sensitive to surprisal were left inferior temporal sulcus ("visual word form area"), bilateral superior temporal gyrus, right amygdala, bilateral anterior temporal poles, and right inferior frontal sulcus. We conclude that prediction during language comprehension can occur at several levels of processing, including at the level of word form. Our study exemplifies the power of combining computational linguistics with cognitive neuroscience, and additionally underlines the feasibility of studying continuous spoken language materials with fMRI. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.
Wagner, J C; Rogers, J E; Baud, R H; Scherrer, J R
A number of compositional Medical Concept Representation systems are being developed. Although these provide for a detailed conceptual representation of the underlying information, they have to be translated back to natural language for used by end-users and applications. The GALEN programme has been developing one such representation and we report here on a tool developed to generate natural language phrases from the GALEN conceptual representations. This tool can be adapted to different source modelling schemes and to different destination languages or sublanguages of a domain. It is based on a multilingual approach to natural language generation, realised through a clean separation of the domain model from the linguistic model and their link by well defined structures. Specific knowledge structures and operations have been developed for bridging between the modelling 'style' of the conceptual representation and natural language. Using the example of the scheme developed for modelling surgical operative procedures within the GALEN-IN-USE project, we show how the generator is adapted to such a scheme. The basic characteristics of the surgical procedures scheme are presented together with the basic principles of the generation tool. Using worked examples, we discuss the transformation operations which change the initial source representation into a form which can more directly be translated to a given natural language. In particular, the linguistic knowledge which has to be introduced--such as definitions of concepts and relationships is described. We explain the overall generator strategy and how particular transformation operations are triggered by language-dependent and conceptual parameters. Results are shown for generated French phrases corresponding to surgical procedures from the urology domain.
Where Humans Meet Machines: Innovative Solutions for Knotty Natural-Language Problems brings humans and machines closer together by showing how linguistic complexities that confound the speech systems of today can be handled effectively by sophisticated natural-language technology. Some of the most vexing natural-language problems that are addressed in this book entail recognizing and processing idiomatic expressions, understanding metaphors, matching an anaphor correctly with its antecedent, performing word-sense disambiguation, and handling out-of-vocabulary words and phrases. This fourteen-chapter anthology consists of contributions from industry scientists and from academicians working at major universities in North America and Europe. They include researchers who have played a central role in DARPA-funded programs and developers who craft real-world solutions for corporations. These contributing authors analyze the role of natural language technology in the global marketplace; they explore the need f...
Waltz, David L
Natural language understanding is central to the goals of artificial intelligence. Any truly intelligent machine must be capable of carrying on a conversation: dialogue, particularly clarification dialogue, is essential if we are to avoid disasters caused by the misunderstanding of the intelligent interactive systems of the future. This book is an interim report on the grand enterprise of devising a machine that can use natural language as fluently as a human. What has really been achieved since this goal was first formulated in Turing's famous test? What obstacles still need to be overcome?
This book discusses the following: Computational Linguistics, Artificial Intelligence, Linguistics, Philosophy, and Cognitive Science and the current state of natural language understanding. Three topics form the focus for discussion; these topics include aspects of grammars, aspects of semantics/pragmatics, and knowledge representation.
The present dissertation reports on research into the nature of Pragmatic Language Impairment (PLI) in children aged 4 to 7 in the Netherlands. First, the possibility of screening for PLI in the general population is examined. Results show that this is indeed possible as well as feasible. Second, an
Many natural language dialogue systems make use of `canned text' for output generation. This approach may be su±cient for dialogues in restricted domains where system utterances are short and simple and use fixed expressions (e.g., slot filling dialogues in the ticket reservation or travel
van Luin, J.; Nijholt, Antinus; op den Akker, Hendrikus J.A.; Giagourta, V.; Strintzis, M.G.
We describe our work on designing a natural language accessible navigation agent for a virtual reality (VR) environment. The agent is part of an agent framework, which means that it can communicate with other agents. Its navigation task consists of guiding the visitors in the environment and to
To identify the neural components that make a brain ready for language, it is important to have well defined linguistic phenotypes, to know precisely what language is. There are two central features to language: the capacity to form signs (words), and the capacity to combine them into complex structures. We must determine how the human brain enables these capacities. A sign is a link between a perceptual form and a conceptual meaning. Acoustic elements and content elements, are already brain-internal in non-human animals, but as categorical systems linked with brain-external elements. Being indexically tied to objects of the world, they cannot freely link to form signs. A crucial property of a language-ready brain is the capacity to process perceptual forms and contents offline, detached from any brain-external phenomena, so their "representations" may be linked into signs. These brain systems appear to have pleiotropic effects on a variety of phenotypic traits and not to be specifically designed for language. Syntax combines signs, so the combination of two signs operates simultaneously on their meaning and form. The operation combining the meanings long antedates its function in language: the primitive mode of predication operative in representing some information about an object. The combination of the forms is enabled by the capacity of the brain to segment vocal and visual information into discrete elements. Discrete temporal units have order and juxtaposition, and vocal units have intonation, length, and stress. These are primitive combinatorial processes. So the prior properties of the physical and conceptual elements of the sign introduce combinatoriality into the linguistic system, and from these primitive combinatorial systems derive concatenation in phonology and combination in morphosyntax. Given the nature of language, a key feature to our understanding of the language-ready brain is to be found in the mechanisms in human brains that enable the unique
Full Text Available To identify the neural components that make a brain ready for language, it is important to have well defined linguistic phenotypes, to know precisely what language is. There are two central features to language: the capacity to form signs (words, and the capacity to combine them into complex structures. We must determine how the human brain enables these capacities.A sign is a link between a perceptual form and a conceptual meaning. Acoustic elements and content elements, are already brain-internal in non-human animals, but as categorical systems linked with brain-external elements. Being indexically tied to objects of the world, they cannot freely link to form signs. A crucial property of a language-ready brain is the capacity to process perceptual forms and contents offline, detached from any brain-external phenomena, so their representations may be linked into signs. These brain systems appear to have pleiotropic effects on a variety of phenotypic traits and not to be specifically designed for language.Syntax combines signs, so the combination of two signs operates simultaneously on their meaning and form. The operation combining the meanings long antedates its function in language: the primitive mode of predication operative in representing some information about an object. The combination of the forms is enabled by the capacity of the brain to segment vocal and visual information into discrete elements. Discrete temporal units have order and juxtaposition, and vocal units have intonation, length, and stress. These are primitive combinatorial processes. So the prior properties of the physical and conceptual elements of the sign introduce combinatoriality into the linguistic system, and from these primitive combinatorial systems derive concatenation in phonology and combination in morphosyntax.Given the nature of language, a key feature to our understanding of the language-ready brain is to be found in the mechanisms in human brains that
Heger, A.S.; Koen, B.V.
A natural language interface has been developed for access to information from a data base, simulating a nuclear plant reliability data system (NPRDS), one of the several existing data bases serving the nuclear industry. In the last decade, the importance of information has been demonstrated by the impressive diffusion of data base management systems. The present methods that are employed to access data bases fall into two main categories of menu-driven systems and use of data base manipulation languages. Both of these methods are currently used by NPRDS. These methods have proven to be tedious, however, and require extensive training by the user for effective utilization of the data base. Artificial intelligence techniques have been used in the development of several intelligent front ends for data bases in nonnuclear domains. Lunar is a natural language program for interface to a data base describing moon rock samples brought back by Apollo. Intellect is one of the first data base question-answering systems that was commercially available in the financial area. Ladder is an intelligent data base interface that was developed as a management aid to Navy decision makers. A natural language interface for nuclear data bases that can be used by nonprogrammers with little or no training provides a means for achieving this goal for this industry
Kambayashi, Shaw; Uenaka, Junji
In this report, a natural language analyzer and two different task planning systems are described. In 1988, we have introduced a Japanese language analyzer named CS-PARSER for the input interface of the task planning system in the Human Acts Simulation Program (HASP). For the purpose of a high speed analysis, we have modified a dictionary system of the CS-PARSER by using C language description. It is found that the new dictionary system is very useful for a high speed analysis and an efficient maintenance of the dictionary. For the study of the task planning problem, we have modified a story generating system named Micro TALE-SPIN to generate a story written in Japanese sentences. We have also constructed a planning system with natural language interface by using the CS-PARSER. Task planning processes and related knowledge bases of these systems are explained. A concept design for a new task planning system will be also discussed from evaluations of above mentioned systems. (author)
Vandeventer Faltin, Anne
Full Text Available This paper illustrates the usefulness of natural language processing (NLP tools for computer assisted language learning (CALL through the presentation of three NLP tools integrated within a CALL software for French. These tools are (i a sentence structure viewer; (ii an error diagnosis system; and (iii a conjugation tool. The sentence structure viewer helps language learners grasp the structure of a sentence, by providing lexical and grammatical information. This information is derived from a deep syntactic analysis. Two different outputs are presented. The error diagnosis system is composed of a spell checker, a grammar checker, and a coherence checker. The spell checker makes use of alpha-codes, phonological reinterpretation, and some ad hoc rules to provide correction proposals. The grammar checker employs constraint relaxation and phonological reinterpretation as diagnosis techniques. The coherence checker compares the underlying "semantic" structures of a stored answer and of the learners' input to detect semantic discrepancies. The conjugation tool is a resource with enhanced capabilities when put on an electronic format, enabling searches from inflected and ambiguous verb forms.
Cawsey, A J; Webber, B L; Jones, R B
Good communication is vital in health care, both among health care professionals, and between health care professionals and their patients. And well-written documents, describing and/or explaining the information in structured databases may be easier to comprehend, more edifying, and even more convincing than the structured data, even when presented in tabular or graphic form. Documents may be automatically generated from structured data, using techniques from the field of natural language generation. These techniques are concerned with how the content, organization and language used in a document can be dynamically selected, depending on the audience and context. They have been used to generate health education materials, explanations and critiques in decision support systems, and medical reports and progress notes.
Jamil, Hasan M
One of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. It may be argued that from a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations. Natural language interfaces for databases are generally effective when they are tuned to the underlying database schema and its semantics. Therefore, changes in database schema become impossible to support, or a substantial reorganization cost must be absorbed to reflect any change. We leverage developments in natural language parsing, rule languages and ontologies, and data integration technologies to assemble a prototype query processor that is able to transform a natural language query into a semantically equivalent structured query over the database. We allow knowledge rules and their frequent modifications as part of the underlying database schema. The approach we adopt in our plug-in overcomes some of the serious limitations of many contemporary natural language interfaces, including support for schema modifications and independence from underlying database schema. The plug-in introduced in this paper is generic and facilitates connecting user selected natural language interfaces to arbitrary databases using a
Hovy, Dirk; Spruit, Shannon
Research in natural language processing (NLP) used to be mostly performed on anonymous corpora, with the goal of enriching linguistic analysis. Authors were either largely unknown or public figures. As we increasingly use more data from social media, this situation has changed: users are now...... individually identifiable, and the outcome of NLP experiments and applications can have a direct effect on their lives. This change should spawn a debate about the ethical implications of NLP, but until now, the internal discourse in the field has not followed the technological development. This position paper...
Gevarter, William B.
Computer-based Natural Language Processing (NLP) is the key to enabling humans and their computer-based creations to interact with machines using natural languages (English, Japanese, German, etc.) rather than formal computer languages. NLP is a major research area in the fields of artificial intelligence and computational linguistics. Commercial…
Andreasen, Troels; Bulskov, Henrik; Nilsson, Jørgen Fischer
This paper makes a case for adopting appropriate forms of natural logic as target language for computational reasoning with descriptive natural language. Natural logics are stylized fragments of natural language where reasoning can be conducted directly by natural reasoning rules reflecting intui...... intuitive reasoning in natural language. The approach taken in this paper is to extend natural logic stepwise with a view to covering successively larger parts of natural language. We envisage applications for computational querying and reasoning, in particular within the life-sciences....
Waltz, D. L.; Maran, L. R.; Dorfman, M. H.; Dinitz, R.; Farwell, D.
During this contract period the authors have: (1) continued investigation of events and actions by means of representation schemes called 'event shape diagrams'; (2) written a parsing program which selects appropriate word and sentence meanings by a parallel process know as activation and inhibition; (3) begun investigation of the point of a story or event by modeling the motivations and emotional behaviors of story characters; (4) started work on combining and translating two machine-readable dictionaries into a lexicon and knowledge base which will form an integral part of our natural language understanding programs; (5) made substantial progress toward a general model for the representation of cognitive relations by comparing English scene and event descriptions with similar descriptions in other languages; (6) constructed a general model for the representation of tense and aspect of verbs; (7) made progress toward the design of an integrated robotics system which accepts English requests, and uses visual and tactile inputs in making decisions and learning new tasks.
Full Text Available This paper presents how to search mathematical formulae written in MathML when given plain words as a query. Since the proposed method allows natural language queries like the traditional Information Retrieval for the mathematical formula search, users do not need to enter any complicated math symbols and to use any formula input tool. For this, formula data is converted into plain texts, and features are extracted from the converted texts. In our experiments, we achieve an outstanding performance, a MRR of 0.659. In addition, we introduce how to utilize formula classification for formula search. By using class information, we finally achieve an improved performance, a MRR of 0.690.
Hovy, Dirk; Spruit, Shannon
Research in natural language processing (NLP) used to be mostly performed on anonymous corpora, with the goal of enriching linguistic analysis. Authors were either largely unknown or public figures. As we increasingly use more data from social media, this situation has changed: users are now...... individually identifiable, and the outcome of NLP experiments and applications can have a direct effect on their lives. This change should spawn a debate about the ethical implications of NLP, but until now, the internal discourse in the field has not followed the technological development. This position paper...... identifies a number of social implications that NLP research may have, and discusses their ethical significance, as well as ways to address them....
Full Text Available We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2010, the authors introduce such a model (the CSC model based on tensor product composition. While this algorithm has many advantages, its implementation is hampered by the large classical computational resources that it requires. In this work we show how computational shortcomings of the CSC approach could be resolved using quantum computation (possibly in addition to existing techniques for dimension reduction. We address the value of quantum RAM (Giovannetti,2008 for this model and extend an algorithm from Wiebe, Braun and Lloyd (2012 into a quantum algorithm to categorize sentences in CSC. Our new algorithm demonstrates a quadratic speedup over classical methods under certain conditions.
Modeling the entailment relation over sentences is one of the generic problems of natural language understanding. In order to account for this problem, we design a theorem prover for Natural Logic, a logic whose terms resemble natural language expressions. The prover is based on an analytic tableau
.... Initiated in 2004 at Defense Research and Development Canada (DRDC), the SACOT knowledge engineering research project is currently investigating, developing and validating innovative natural language processing (NLP...
Langkopf, B.S.; Mallory, L.H.
A scientific data base, the Tuff Data Base, is being created at Sandia National Laboratories on the Cyber 170/855, using System 2000. It is being developed for use by scientists and engineers investigating the feasibility of locating a high-level radioactive waste repository in tuff (a type of volcanic rock) at Yucca Mountain on and adjacent to the Nevada Test Site. This project, the Nevada Nuclear Waste Storage Investigations (NNWSI) Project, is managed by the Nevada Operations Office of the US Department of Energy. A user-friendly interface, PRIMER, was developed that uses the Self-Contained Facility (SCF) command SUBMIT and System 2000 Natural Language functions and parametric strings that are schema resident. The interface was designed to: (1) allow users, with or without computer experience or keyboard skill, to sporadically access data in the Tuff Data Base; (2) produce retrieval capabilities for the user quickly; and (3) acquaint the users with the data in the Tuff Data Base. This paper gives a brief description of the Tuff Data Base Schema and the interface, PRIMER, which is written in Fortran V. 3 figures
The Policy-Based Management Natural Language Parser (PBEM) is a rules-based approach to enterprise management that can be used to automate certain management tasks. This parser simplifies the management of a given endeavor by establishing policies to deal with situations that are likely to occur. Policies are operating rules that can be referred to as a means of maintaining order, security, consistency, or other ways of successfully furthering a goal or mission. PBEM provides a way of managing configuration of network elements, applications, and processes via a set of high-level rules or business policies rather than managing individual elements, thus switching the control to a higher level. This software allows unique management rules (or commands) to be specified and applied to a cross-section of the Global Information Grid (GIG). This software embodies a parser that is capable of recognizing and understanding conversational English. Because all possible dialect variants cannot be anticipated, a unique capability was developed that parses passed on conversation intent rather than the exact way the words are used. This software can increase productivity by enabling a user to converse with the system in conversational English to define network policies. PBEM can be used in both manned and unmanned science-gathering programs. Because policy statements can be domain-independent, this software can be applied equally to a wide variety of applications.
Paul H Thibodeau
Full Text Available Metaphors pervade discussions of social issues like climate change, the economy, and crime. We ask how natural language metaphors shape the way people reason about such social issues. In previous work, we showed that describing crime metaphorically as a beast or a virus, led people to generate different solutions to a city's crime problem. In the current series of studies, instead of asking people to generate a solution on their own, we provided them with a selection of possible solutions and asked them to choose the best ones. We found that metaphors influenced people's reasoning even when they had a set of options available to compare and select among. These findings suggest that metaphors can influence not just what solution comes to mind first, but also which solution people think is best, even when given the opportunity to explicitly compare alternatives. Further, we tested whether participants were aware of the metaphor. We found that very few participants thought the metaphor played an important part in their decision. Further, participants who had no explicit memory of the metaphor were just as much affected by the metaphor as participants who were able to remember the metaphorical frame. These findings suggest that metaphors can act covertly in reasoning. Finally, we examined the role of political affiliation on reasoning about crime. The results confirm our previous findings that Republicans are more likely to generate enforcement and punishment solutions for dealing with crime, and are less swayed by metaphor than are Democrats or Independents.
When we think of everyday language use, the first things that come to mind include colloquial conversations, reading and writing e-mails, sending text messages or reading a book. But can we study the brain basis of language as we use it in our daily lives? As a topic of study, the cognitive
593], pages International Conference of the IEEE Engineer- 351-363. ing in Medicine and Biology Society, volume 3, pages 1347-1348, New Orleans, LA...Conference on Machine Translation of Languages and Applied  Ingrid Zukerman. Koalas are not bears: Gener- Language Analysis. pages 66-80. Her
Kyle, Kristopher; Crossley, Scott A.
This study explores the construct of lexical sophistication and its applications for measuring second language lexical and speaking proficiency. In doing so, the study introduces the Tool for the Automatic Analysis of LExical Sophistication (TAALES), which calculates text scores for 135 classic and newly developed lexical indices related to word…
Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.
Hamon, Thierry; Mougin, Fleur; Grabar, Natalia
With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description. The method is designed on 50 questions over 3 biomedical knowledge bases, and evaluated on 27 questions. It achieves 0.78 F-measure on the test set. The method for translating natural language questions into SPARQL queries is implemented as Perl module available at http://search.cpan.org/ thhamon/RDF-NLP-SPARQLQuery.
Dougherty, Ray C
This book's main goal is to show readers how to use the linguistic theory of Noam Chomsky, called Universal Grammar, to represent English, French, and German on a computer using the Prolog computer language. In so doing, it presents a follow-the-dots approach to natural language processing, linguistic theory, artificial intelligence, and expert systems. The basic idea is to introduce meaningful answers to significant problems involved in representing human language data on a computer. The book offers a hands-on approach to anyone who wishes to gain a perspective on natural language
Wakker, P.P.; Sarin, R.H.
Machina & Schmeidler (Econometrica, 60, 1992) gave preference conditions for probabilistic sophistication, i.e. decision making where uncertainty can be expressed in terms of (subjective) probabilities without commitment to expected utility maximization. This note shows that simpler and more general
Dominick, Wayne D. (Editor); Liu, I-Hsiung
The currently developed user language interfaces of information systems are generally intended for serious users. These interfaces commonly ignore potentially the largest user group, i.e., casual users. This project discusses the concepts and implementations of a natural query language system which satisfy the nature and information needs of casual users by allowing them to communicate with the system in the form of their native (natural) language. In addition, a framework for the development of such an interface is also introduced for the MADAM (Multics Approach to Data Access and Management) system at the University of Southwestern Louisiana.
May 26, 2018 ... resent, and store information in a natural-language-inde- pendent format . UNL is .... account semantic information available in words of the problem ...... Sentiment Analysis (SA) plays a vital role in decision making process.
Full Text Available Recent mathematical and algorithmic results in the field of finite-state technology, as well the increase in computing power, have constructed the base for a new approach in natural language processing. However the task of creating an appropriate model that would describe the phenomena of the natural language is still to be achieved. ln this paper I'm presenting some notions related to the finite-state modelling of syntax and morphology.
Institute for the Study of Violent Groups NATO North Atlantic Treaty Organization NLP Natural Language Processing PCorpus Permanent Corpus PDF...approaches, we apply Natural Language Processing ( NLP ) tools to a unique database of text documents collected by Whiteside (2014). His collection...from Arabic to English. Compared to other terrorism databases, Whiteside’s collection methodology limits the scope of the database and avoids coding
Full Text Available Arabic is a Semitic language spoken by more than 330 million people as a native language, in an area extending from the Arabian/Persian Gulf in the East to the Atlantic Ocean in the West. Moreover, it is the language in which 1.4 billion Muslims around the world perform their daily prayers. Over the last few years, Arabic natural language processing (ANLP has gained increasing importance, and several state of the art systems have been developed for a wide range of applications.
Garfield, D A; Rapp, C; Evens, M
The potential benefit of artificial intelligence (AI) technology as a tool of psychiatry has not been well defined. In this essay, the technology of natural language processing and its position with regard to the two main schools of AI is clearly outlined. Past experiments utilizing AI techniques in understanding psychopathology are reviewed. Natural language processing can automate the analysis of transcripts and can be used in modeling theories of language comprehension. In these ways, it can serve as a tool in testing psychological theories of psychopathology and can be used as an effective tool in empirical research on verbal behavior in psychopathology.
Using contemporary science, the paper builds on Wittgenstein’s views of human language. Rather than ascribing reality to inscription-like entities, it links embodiment with distributed cognition. The verbal or (quasi) technological aspect of language is traced to not action, but human specific...... interactivity. This species-specific form of sense-making sustains, among other things, using texts, making/construing phonetic gestures and thinking. Human action is thus grounded in appraisals or sense-saturated coordination. To illustrate interactivity at work, the paper focuses on a case study. Over 11 s......, a crime scene investigator infers that she is probably dealing with an inside job: she uses not words, but intelligent gaze. This connects professional expertise to circumstances and the feeling of thinking. It is suggested that, as for other species, human appraisal is based in synergies. However, since...
Gevarter, W. B.
Computer based Natural Language Processing (NLP) is the key to enabling humans and their computer based creations to interact with machines in natural language (like English, Japanese, German, etc., in contrast to formal computer languages). The doors that such an achievement can open have made this a major research area in Artificial Intelligence and Computational Linguistics. Commercial natural language interfaces to computers have recently entered the market and future looks bright for other applications as well. This report reviews the basic approaches to such systems, the techniques utilized, applications, the state of the art of the technology, issues and research requirements, the major participants and finally, future trends and expectations. It is anticipated that this report will prove useful to engineering and research managers, potential users, and others who will be affected by this field as it unfolds.
Olive, Joseph P; McCary, John
This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program - The Global Autonomous Language Exploitation within the Defense Advanced Research Projects Agency (DARPA), while placing it in the context of previous research in the fields of natural language and signal processing, artificial intelligence and machine translation. The most fundamental contrast between GALE and its predecessor programs was its holistic integration of previously separate or sequential processes. In earlier language research pro
Hiemstra, Djoerd; de Jong, Franciska M.G.
Traditionally, natural language processing techniques for information retrieval have always been studied outside the framework of formal models of information retrieval. In this article, we introduce a new formal model of information retrieval based on the application of statistical language models.
Widemann, David P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Wang, Eric X. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Thiagarajan, Jayaraman J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and the IMDB Movie Review dataset.
Full Text Available How human language arose is a mystery in the evolution of Homo sapiens. Miyagawa, Berwick, & Okanoya (Frontiers 2013 put forward a proposal, which we will call the Integration Hypothesis of human language evolution, which holds that human language is composed of two components, E for expressive, and L for lexical. Each component has an antecedent in nature: E as found, for example, in birdsong, and L in, for example, the alarm calls of monkeys. E and L integrated uniquely in humans to give rise to language. A challenge to the Integration Hypothesis is that while these non-human systems are finite-state in nature, human language is known to require characterization by a non-finite state grammar. Our claim is that E and L, taken separately, are finite-state; when a grammatical process crosses the boundary between E and L, it gives rise to the non-finite state character of human language. We provide empirical evidence for the Integration Hypothesis by showing that certain processes found in contemporary languages that have been characterized as non-finite state in nature can in fact be shown to be finite-state. We also speculate on how human language actually arose in evolution through the lens of the Integration Hypothesis.
Névéol, Aurélie; Dalianis, Hercules; Velupillai, Sumithra; Savova, Guergana; Zweigenbaum, Pierre
Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.
Allred, Sarah; Duffy, Sean; Smith, John
We study the relationship between the cognitive load manipulation and strategic sophistication. The cognitive load manipulation is designed to reduce the subject's cognitive resources that are available for deliberation on a choice. In our experiment, subjects are placed under a large cognitive load (given a difficult number to remember) or a low cognitive load (given a number which is not difficult to remember). Subsequently, the subjects play a one-shot game then they are asked to recall...
Supervision Distant supervision is a recent trend in information extraction. Distantly-supervised extractors are trained using a corpus of unlabeled text...consists of fill-in-the-blank natural language questions such as “Incan emperor ” or “Cunningham directed Auchtre’s second music video .” These questions...with an 132 unknown knowledge base, simultaneously learning how to semantically parse language and pop - ulate the knowledge base. The weakly
Gevarter, W. B.
An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.
Antonio Gisolfi; Enrico Fischetti
The aim of this paper is to show that with a subset of a natural language, simple systems running on PCs can be developed that can nevertheless be an effective tool for interfacing purposes in the building of an Intelligent Tutoring System (ITS). After presenting the special characteristics of the Smalltalk/V language, which provides an appropriate environment for the development of an interface, the overall architecture of the interface module is discussed. We then show how sentences are par...
Researchers, motivated by the need to improve the efficiency of natural language processing tools to handle web-scale data, have recently arrived at models that remarkably match the expected features of human language processing under the Now-or-Never bottleneck framework. This provides additional support for said framework and highlights the research potential in the interaction between applied computational linguistics and cognitive science.
This paper introduces natural language expressions and expert's subjectivity to system reliability analysis. To this end, this paper defines a subjective measure of reliability and presents the method of the system reliability analysis using the measure. The subjective measure of reliability corresponds to natural language expressions of reliability estimation, which is represented by a fuzzy set defined on [0,1]. The presented method deals with the dependence among subsystems and employs parametrized operations of subjective measures of reliability which can reflect expert 's subjectivity towards the analyzed system. The analysis results are also expressed by linguistic terms. Finally this paper gives an example of the system reliability analysis by the presented method
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work.The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as tw
Full Text Available There are some factors regarding which aspect of second language acquisition is affected by individual learner factors, age, learning style. aptitude, motivation, and personality. This research is about English language acquisition of fourth-year child by nature and nurture. The child acquired her second language acquisition at home and also in one of the courses in Jakarta. She schooled by her parents in order to be able to speak English well as a target language for her future time. The purpose of this paper is to see and examine individual learner difference especially in using English as a second language. This study is a library research and retrieved data collected, recorded, transcribed, and analyzed descriptively. The results can be concluded: the child is able to communicate well and also able to construct simple sentences, complex sentences, sentence statement, phrase questions, and explain something when her teacher asks her at school. She is able to communicate by making a simple sentence or compound sentence in well-form (two clauses or three clauses, even though she still not focus to use the past tense form and sometimes she forgets to put bound morpheme -s in third person singular but she can use turn-taking in her utterances. It is a very long process since the child does the second language acquisition. The family and teacher should participate and assist the child, the proven child can learn the first and the second language at the same time.
Anne E. Thessen
A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters, but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.
Michael, Joel; Rovick, Allen; Glass, Michael; Zhou, Yujian; Evens, Martha
CIRCSIM-Tutor is a computer tutor designed to carry out a natural language dialogue with a medical student. Its domain is the baroreceptor reflex, the part of the cardiovascular system that is responsible for maintaining a constant blood pressure. CIRCSIM-Tutor's interaction with students is modeled after the tutoring behavior of two experienced…
Doszkocs, Tamas E.
The National Library of Medicine's Current Information Transfer in English public access online catalog offers unique subject search capabilities--natural-language query input, automatic medical subject headings display, closest match search strategy, ranked document output, dynamic end user feedback for search refinement. References, description…
Szymczak, Bartlomiej Antoni
tried to establish a domain independent “ontological semantics” for relevant fragments of natural language. The purpose of this research is to develop methods and systems for taking advantage of formal ontologies for the purpose of extracting the meaning contents of texts. This functionality...
Dadlez, Eva M.
Describes a natural language searching strategy for retrieving current material which has bearing on George Orwell's "1984," and identifies four main themes (technology, authoritarianism, press and psychological/linguistic implications of surveillance, political oppression) which have emerged from cross-database searches of the "Big…
It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…
Rodríguez, J. Tinguaro; Franco, Camilo; Montero, Javier
The evidence coming from cognitive psychology and linguistics shows that pairs of reference concepts (as e.g. good/bad, tall/short, nice/ugly, etc.) play a crucial role in the way we everyday use and understand natural languages in order to analyze reality and make decisions. Different situations...
van der Sluis, Ielka; Hielkema, F.; Mellish, C.; Doherty, G.
In this paper we look at what may be learned from a comparative study examining non-technical users with a background in social science browsing and querying metadata. Four query tasks were carried out with a natural language interface and with an interface that uses a web paradigm with hyperlinks.
Nikora, Allen P.
This viewgraph presentation reviews the rationale of the program to transform natural language specifications into formal notation.Specifically, automate generation of Linear Temporal Logic (LTL)correctness properties from natural language temporal specifications. There are several reasons for this approach (1) Model-based techniques becoming more widely accepted, (2) Analytical verification techniques (e.g., model checking, theorem proving) significantly more effective at detecting types of specification design errors (e.g., race conditions, deadlock) than manual inspection, (3) Many requirements still written in natural language, which results in a high learning curve for specification languages, associated tools and increased schedule and budget pressure on projects reduce training opportunities for engineers, and (4) Formulation of correctness properties for system models can be a difficult problem. This has relevance to NASA in that it would simplify development of formal correctness properties, lead to more widespread use of model-based specification, design techniques, assist in earlier identification of defects and reduce residual defect content for space mission software systems. The presentation also discusses: potential applications, accomplishments and/or technological transfer potential and the next steps.
Komata, Masaoki; Oosawa, Yasuo; Ujita, Hiroshi
A natural language retrieval program NATLANG is developed to assist in the retrieval of information from event-and-cause descriptions in Licensee Event Reports (LER). The characteristics of NATLANG are (1) the use of base forms of words to retrieve related forms altered by the addition of prefixes or suffixes or changes in inflection, (2) direct access and short time retrieval with an alphabet pointer, (3) effective determination of the items and entries for a Hitachi event classification in a two step retrieval scheme, and (4) Japanese character output with the PL-1 language. NATLANG output reduces the effort needed to re-classify licensee events in the Hitachi event classification. (author)
Full Text Available This paper shows how fieldwork data can be managed using the program Toolbox together with the Natural Language Toolkit (NLTK for the Python programming language. It provides background information about Toolbox and describes how it can be downloaded and installed. The basic functionality of the program for lexicons and texts is described, and its strengths and weaknesses are reviewed. Its underlying data format is briefly discussed, and Toolbox processing capabilities of NLTK are introduced, showing ways in which it can be used to extend the functionality of Toolbox. This is illustrated with a few simple scripts that demonstrate basic data management tasks relevant to language documentation, such as printing out the contents of a lexicon as HTML.
This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…
Smith, D.; Lewins, J.D.
The art of the direct method of Liapunov to determine system stability is to construct a suitable Liapunov or V function where V is to be positive definite (PD), to shrink to a center, which may be conveniently chosen as the origin, and where V is the negative definite (ND). One aid to the art is to solve an approximation to the system equations in order to provide a candidate V function. It can happen, however, that the V function is not strictly ND but vanishes at a finite number of isolated points. Naively, one anticipates that stability has been demonstrated since the trajectory of the system at such points is only momentarily tangential and immediately enters a region of inward directed trajectories. To demonstrate stability rigorously requires the construction of a sophisticated Liapunov function from what can be called the naive original choice. In this paper, the authors demonstrate the method of perturbing the naive function in the context of the well-known second-order oscillator and then apply the method to a more complicated problem based on a prompt jump model for a nuclear fission reactor
Nastassja A. Lewinski
Full Text Available Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.
Full Text Available It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.
ELEMENT. PROJECT. TASKN Artificial Inteligence Laboratory A1A4WR NTumet 0) 545 Technology Square Cambridge, MA 02139 Ln *t- CONTROLLING OFFICE NAME AND...RO-RI95 922 EXPLOITING LEXICAL REGULARITIES IN DESIGNING NATURAL 1/1 LANGUAGE SYSTENS(U) MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE...oes.ary and ftdou.Ip hr Nl wow" L,2This paper presents the lexical component of the START Question Answering system developed at the MIT Artificial
studies: the Time-Triggered Ethernet (TTEthernet) communication platform used in space, and FAA-Isolette infant incubators used in NICU . We...in space, and FAA-Isolette infant incubators used in Neonatal Intensive Care Units ( NICUs ). We systematically evalu- ated various aspects of ARSENAL...effect, we present the ARSENAL methodology. ARSENAL uses state-of-the-art advances in natural language processing (NLP) and formal methods (FM) to
Friedman, Carol; Hripcsak, George; Shagina, Lyuda; Liu, Hongfang
Objective: To design a document model that provides reliable and efficient access to clinical information in patient reports for a broad range of clinical applications, and to implement an automated method using natural language processing that maps textual reports to a form consistent with the model. Methods: A document model that encodes structured clinical information in patient reports while retaining the original contents was designed using the extensible markup language (XML), and a document type definition (DTD) was created. An existing natural language processor (NLP) was modified to generate output consistent with the model. Two hundred reports were processed using the modified NLP system, and the XML output that was generated was validated using an XML validating parser. Results: The modified NLP system successfully processed all 200 reports. The output of one report was invalid, and 199 reports were valid XML forms consistent with the DTD. Conclusions: Natural language processing can be used to automatically create an enriched document that contains a structured component whose elements are linked to portions of the original textual report. This integrated document model provides a representation where documents containing specific information can be accurately and efficiently retrieved by querying the structured components. If manual review of the documents is desired, the salient information in the original reports can also be identified and highlighted. Using an XML model of tagging provides an additional benefit in that software tools that manipulate XML documents are readily available. PMID:9925230
de Dreu, J.|info:eu-repo/dai/nl/364537906; Bikker, J.A.|info:eu-repo/dai/nl/06912261X
This paper assesses the sophistication of pension funds’ investment policies using data on 748 Dutch pension funds during the 1999–2006 period. We develop three indicators of sophistication: gross rounding of investment choices, investments in alternative sophisticated asset classes and ‘home bias’.
Cai, Tianrun; Giannopoulos, Andreas A.; Yu, Sheng; Kelil, Tatiana; Ripley, Beth; Kumamaru, Kanako K.; Rybicki, Frank J.
The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively “mine” these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. “Intelligent” search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. ©RSNA, 2016 PMID:26761536
Cai, Tianrun; Giannopoulos, Andreas A; Yu, Sheng; Kelil, Tatiana; Ripley, Beth; Kumamaru, Kanako K; Rybicki, Frank J; Mitsouras, Dimitrios
The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. ©RSNA, 2016.
Maurice H. P. M. van Putten
Full Text Available We consider the rate R and variance σ 2 of Shannon information in snippets of text based on word frequencies in the natural language. We empirically identify Kolmogorov’s scaling law in σ 2 ∝ k - 1 . 66 ± 0 . 12 (95% c.l. as a function of k = 1 / N measured by word count N. This result highlights a potential association of information flow in snippets, analogous to energy cascade in turbulent eddies in fluids at high Reynolds numbers. We propose R and σ 2 as robust utility functions for objective ranking of concordances in efficient search for maximal information seamlessly across different languages and as a starting point for artificial attention.
Full Text Available The aim of this paper is to show that with a subset of a natural language, simple systems running on PCs can be developed that can nevertheless be an effective tool for interfacing purposes in the building of an Intelligent Tutoring System (ITS. After presenting the special characteristics of the Smalltalk/V language, which provides an appropriate environment for the development of an interface, the overall architecture of the interface module is discussed. We then show how sentences are parsed by the interface, and how interaction takes place with the user. The knowledge-acquisition phase is subsequently described. Finally, some excerpts from a tutoring session concerned with elementary geometry are discussed, and some of the problems and limitations of the approach are illustrated.
Shah, Nishal Pradeepkumar
A recent advance in computer technology has permitted scientists to implement and test algorithms that were known from quite some time (or not) but which were computationally expensive. Two such projects are IBM's Jeopardy as a part of its DeepQA project  and Wolfram's Wolframalpha. Both these methods implement natural language processing (another goal of AI scientists) and try to answer questions as asked by the user. Though the goal of the two projects is similar, both of them have a ...
Bochkarev, Vladimir V.; Lerner, Eduard Yu; Shevlyakova, Anna V.
This paper is devoted to verifying of the empirical Zipf and Hips laws in natural languages using Google Books Ngram corpus data. The connection between the Zipf and Heaps law which predicts the power dependence of the vocabulary size on the text size is discussed. In fact, the Heaps exponent in this dependence varies with the increasing of the text corpus. To explain it, the obtained results are compared with the probability model of text generation. Quasi-periodic variations with characteristic time periods of 60-100 years were also found.
Bochkarev, Vladimir V; Lerner, Eduard Yu; Shevlyakova, Anna V
This paper is devoted to verifying of the empirical Zipf and Hips laws in natural languages using Google Books Ngram corpus data. The connection between the Zipf and Heaps law which predicts the power dependence of the vocabulary size on the text size is discussed. In fact, the Heaps exponent in this dependence varies with the increasing of the text corpus. To explain it, the obtained results are compared with the probability model of text generation. Quasi-periodic variations with characteristic time periods of 60-100 years were also found
Full Text Available We present a publicly-available state-of-the-art research and development platform for Machine Translation and Natural Language Processing that runs on the Amazon Elastic Compute Cloud. This provides a standardized research environment for all users, and enables perfect reproducibility and compatibility. Box also enables users to use their hardware budget to avoid the management and logistical overhead of maintaining a research lab, yet still participate in global research community with the same state-of-the-art tools.
Nafari, Maryam; Weaver, Chris
Richly interactive visualization tools are increasingly popular for data exploration and analysis in a wide variety of domains. Existing systems and techniques for recording provenance of interaction focus either on comprehensive automated recording of low-level interaction events or on idiosyncratic manual transcription of high-level analysis activities. In this paper, we present the architecture and translation design of a query-to-question (Q2Q) system that automatically records user interactions and presents them semantically using natural language (written English). Q2Q takes advantage of domain knowledge and uses natural language generation (NLG) techniques to translate and transcribe a progression of interactive visualization states into a visual log of styled text that complements and effectively extends the functionality of visualization tools. We present Q2Q as a means to support a cross-examination process in which questions rather than interactions are the focus of analytic reasoning and action. We describe the architecture and implementation of the Q2Q system, discuss key design factors and variations that effect question generation, and present several visualizations that incorporate Q2Q for analysis in a variety of knowledge domains.
Full Text Available Suicide is the second leading cause of death among 25–34 year olds and the third leading cause of death among 15–25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient’s thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.
Pestian, John; Nasrallah, Henry; Matykiewicz, Pawel; Bennett, Aurora; Leenaars, Antoon
Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.
Pons, Ewoud; Braun, Loes M M; Hunink, M G Myriam; Kors, Jan A
Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed. (©) RSNA, 2016 Online supplemental material is available for this article.
This book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE. The authors present a generic architecture for developing systems that are able to learn how to extract relevant information from natural language documents, and illustrate how to implement working systems using state-of-the-art and freely available software tools. The book also discusses concrete applications illustrating IE uses. · Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for t...
Research into natural language understanding systems for computers has concentrated on implementing particular grammars and grammatical models of the language concerned. This paper presents a rationale for research into natural language understanding systems based on neurological and psychological principles. Important features of the approach are that it seeks to place the onus of learning the language on the computer, and that it seeks to make use of the vast wealth of relevant psycholinguistic and neurolinguistic theory. 22 references.
Poletiek, Fenna H; Fitz, Hartmut; Bocanegra, Bruno R
Rey et al. (2012) present data from a study with baboons that they interpret in support of the idea that center-embedded structures in human language have their origin in low level memory mechanisms and associative learning. Critically, the authors claim that the baboons showed a behavioral preference that is consistent with center-embedded sequences over other types of sequences. We argue that the baboons' response patterns suggest that two mechanisms are involved: first, they can be trained to associate a particular response with a particular stimulus, and, second, when faced with two conditioned stimuli in a row, they respond to the most recent one first, copying behavior they had been rewarded for during training. Although Rey et al. (2012) 'experiment shows that the baboons' behavior is driven by low level mechanisms, it is not clear how the animal behavior reported, bears on the phenomenon of Center Embedded structures in human syntax. Hence, (1) natural language syntax may indeed have been shaped by low level mechanisms, and (2) the baboons' behavior is driven by low level stimulus response learning, as Rey et al. propose. But is the second evidence for the first? We will discuss in what ways this study can and cannot give evidential value for explaining the origin of Center Embedded recursion in human grammar. More generally, their study provokes an interesting reflection on the use of animal studies in order to understand features of the human linguistic system. Copyright © 2015 Elsevier B.V. All rights reserved.
This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.
Wu, Joy T; Dernoncourt, Franck; Gehrmann, Sebastian; Tyler, Patrick D; Moseley, Edward T; Carlson, Eric T; Grant, David W; Li, Yeran; Welt, Jonathan; Celi, Leo Anthony
Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies. Copyright © 2017. Published by Elsevier B.V.
Kashyap, Vipul; Turchin, Alexander; Morin, Laura; Chang, Frank; Li, Qi; Hongsermeier, Tonya
Structured Clinical Documentation is a fundamental component of the healthcare enterprise, linking both clinical (e.g., electronic health record, clinical decision support) and administrative functions (e.g., evaluation and management coding, billing). One of the challenges in creating good quality documentation templates has been the inability to address specialized clinical disciplines and adapt to local clinical practices. A one-size-fits-all approach leads to poor adoption and inefficiencies in the documentation process. On the other hand, the cost associated with manual generation of documentation templates is significant. Consequently there is a need for at least partial automation of the template generation process. We propose an approach and methodology for the creation of structured documentation templates for diabetes using Natural Language Processing (NLP).
Deleger, Louise; Li, Qi; Lingren, Todd; Kaiser, Megan; Molnar, Katalin; Stoutenborough, Laura; Kouril, Michal; Marsolo, Keith; Solti, Imre
We present the construction of three annotated corpora to serve as gold standards for medical natural language processing (NLP) tasks. Clinical notes from the medical record, clinical trial announcements, and FDA drug labels are annotated. We report high inter-annotator agreements (overall F-measures between 0.8467 and 0.9176) for the annotation of Personal Health Information (PHI) elements for a de-identification task and of medications, diseases/disorders, and signs/symptoms for information extraction (IE) task. The annotated corpora of clinical trials and FDA labels will be publicly released and to facilitate translational NLP tasks that require cross-corpora interoperability (e.g. clinical trial eligibility screening) their annotation schemas are aligned with a large scale, NIH-funded clinical text annotation project.
Full Text Available Development of information technologies is growing steadily. With the latest software technologies development and application of the methods of artificial intelligence and machine learning intelligence embededs in computers, the expectations are that in near future computers will be able to solve problems themselves like people do. Artificial intelligence emulates human behavior on computers. Rather than executing instructions one by one, as theyare programmed, machine learning employs prior experience/data that is used in the process of system’s training. In this state of the art paper, common methods in AI, such as machine learning, pattern recognition and the natural language processing (NLP are discussed. Also are given standard architecture of NLP processing system and the level thatisneeded for understanding NLP. Lastly the statistical NLP processing and multi-word expressions are described.
Graham, Matthew; Zhang, M.; Djorgovski, S. G.; Donalek, C.; Drake, A. J.; Mahabal, A.
The rapidly emerging field of time domain astronomy is one of the most exciting and vibrant new research frontiers, ranging in scientific scope from studies of the Solar System to extreme relativistic astrophysics and cosmology. It is being enabled by a new generation of large synoptic digital sky surveys - LSST, PanStarrs, CRTS - that cover large areas of sky repeatedly, looking for transient objects and phenomena. One of the biggest challenges facing these is the automated classification of transient events, a process that needs machine-processible astronomical knowledge. Semantic technologies enable the formal representation of concepts and relations within a particular domain. ATELs (http://www.astronomerstelegram.org) are a commonly-used means for reporting and commenting upon new astronomical observations of transient sources (supernovae, stellar outbursts, blazar flares, etc). However, they are loose and unstructured and employ scientific natural language for description: this makes automated processing of them - a necessity within the next decade with petascale data rates - a challenge. Nevertheless they represent a potentially rich corpus of information that could lead to new and valuable insights into transient phenomena. This project lies in the cutting-edge field of astrosemantics, a branch of astroinformatics, which applies semantic technologies to astronomy. The ATELs have been used to develop an appropriate concept scheme - a representation of the information they contain - for transient astronomy using hierarchical clustering of processed natural language. This allows us to automatically organize ATELs based on the vocabulary used. We conclude that we can use simple algorithms to process and extract meaning from astronomical textual data.
Natural language processing (NLP) is a subfield of artificial intelligence and computational linguistics. It studies the problems of automated generation and understanding of natural human languages. This paper outlines a framework to use computer and natural language techniques for various levels of learners to learn foreign languages in Computer-based Learning environment. We propose some ideas for using the computer as a practical tool for learning foreign language where the most of courseware is generated automatically. We then describe how to build Computer Based Learning tools, discuss its effectiveness, and conclude with some possibilities using on-line resources.
Deane, Paul; Sheehan, Kathleen
This paper is an exploration of the conceptual issues that have arisen in the course of building a natural language generation (NLG) system for automatic test item generation. While natural language processing techniques are applicable to general verbal items, mathematics word problems are particularly tractable targets for natural language…
Miller, William R; Johnson, Wendy R
Client motivation for change, a topic of high interest to addiction clinicians, is multidimensional and complex, and many different approaches to measurement have been tried. The current effort drew on psycholinguistic research on natural language that is used by clients to describe their own motivation. Seven addiction treatment sites participated in the development of a simple scale to measure client motivation. Twelve items were drafted to represent six potential dimensions of motivation for change that occur in natural discourse. The maximum self-rating of motivation (10 on a 0-10 scale) was the median score on all items, and 43% of respondents rated 10 on all 12 items - a substantial ceiling effect. From 1035 responses, three factors emerged representing importance, ability, and commitment - constructs that are also reflected in several theoretical models of motivation. A 3-item version of the scale, with one marker item for each of these constructs, accounted for 81% of variance in the full scale. The three items are: 1. It is important for me to . . . 2. I could . . . and 3. I am trying to . . . This offers a quick (1-minute) assessment of clients' self-reported motivation for change.
This study explores language ideologies of English at a Korean university where English has been adopted as an official language. This study draws on ethnographic data in order to understand how speakers respond to and experience the institutional language policy. The findings show that language ideologies in this university represent the…
Wittrock, Merlin C.
Concepts in cognitive psychology are applied to the language used in military situations, and a sentence classification system for use in analyzing military language is outlined. The system is designed to be used, in part, in conjunction with a natural language query system that allows a user to access a database. The discussion of military…
Hirschman, Lynette; Fort, Karën; Boué, Stéphanie; Kyrpides, Nikos; Islamaj Doğan, Rezarta; Cohen, Kevin Bretonnel
Crowdsourcing is increasingly utilized for performing tasks in both natural language processing and biocuration. Although there have been many applications of crowdsourcing in these fields, there have been fewer high-level discussions of the methodology and its applicability to biocuration. This paper explores crowdsourcing for biocuration through several case studies that highlight different ways of leveraging 'the crowd'; these raise issues about the kind(s) of expertise needed, the motivations of participants, and questions related to feasibility, cost and quality. The paper is an outgrowth of a panel session held at BioCreative V (Seville, September 9-11, 2015). The session consisted of four short talks, followed by a discussion. In their talks, the panelists explored the role of expertise and the potential to improve crowd performance by training; the challenge of decomposing tasks to make them amenable to crowdsourcing; and the capture of biological data and metadata through community editing.Database URL: http://www.mitre.org/publications/technical-papers/crowdsourcing-and-curation-perspectives. © The Author(s) 2016. Published by Oxford University Press.
A new approach for processing vowelized and unvowelized Arabic texts in order to prepare them for Natural Language Processing (NLP) purposes is described. The developed approach is rule-based and made up of four phases: text tokenization, word light stemming, word's morphological analysis and text annotation. The first phase preprocesses the input text in order to isolate the words and represent them in a formal way. The second phase applies a light stemmer in order to extract the stem of each word by eliminating the prefixes and suffixes. The third phase is a rule-based morphological analyzer that determines the root and the morphological pattern for each extracted stem. The last phase produces an annotated text where each word is tagged with its morphological attributes. The preprocessor presented in this paper is capable of dealing with vowelized and unvowelized words, and provides the input words along with relevant linguistics information needed by different applications. It is designed to be used with different NLP applications such as machine translation text summarization, text correction, information retrieval and automatic vowelization of Arabic Text. (author)
Juuso, Esko K.
Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.
Wu Stephen T
Full Text Available Abstract Background One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs, thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System versions 2.0 and later. Conclusions We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.
Wu, Stephen T; Kaggal, Vinod C; Dligach, Dmitriy; Masanz, James J; Chen, Pei; Becker, Lee; Chapman, Wendy W; Savova, Guergana K; Liu, Hongfang; Chute, Christopher G
One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.
Appelo, Lisette; Leermakers, M.C.J.; Rous, J.H.G.
A method is described for the generation of related natural-language expressions. The method is based on a formal grammar of the natural language in question, specified in the Controlled M-Grammar (CMG) formalism. In the CMG framework the generation of an utterance is controlled by a derivation
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...
Gullberg, M.; Robert, L.; Dimroth, C.; Veroude, K.; Indefrey, P.
Despite the literature on the role of input in adult second-language (L2) acquisition and on artificial and statistical language learning, surprisingly little is known about how adults break into a new language in the wild. This article reports on a series of behavioral and neuroimaging studies that
Lee, Ming Che; Chang, Jia Wei; Hsieh, Tung Cheng
This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.
McNamara, Danielle S; Crossley, Scott A; Roscoe, Rod
The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (Written Communication 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6% of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38% of the variance in human scores of essay quality with 91% adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44% of the variance with 94% adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.
Haug Peter J
Full Text Available Abstract Background The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. Methods For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular. We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. Results The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients, but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. Conclusion The global aim of our project is to automate the process of creating and maintaining a problem
Redd, Andrew; Pickard, Steve; Meystre, Stephane; Scehnet, Jeffrey; Bolton, Dan; Heavirland, Julia; Weaver, Allison Lynn; Hope, Carol; Garvin, Jennifer Hornung
We introduce and evaluate a new, easily accessible tool using a common statistical analysis and business analytics software suite, SAS, which can be programmed to remove specific protected health information (PHI) from a text document. Removal of PHI is important because the quantity of text documents used for research with natural language processing (NLP) is increasing. When using existing data for research, an investigator must remove all PHI not needed for the research to comply with human subjects' right to privacy. This process is similar, but not identical, to de-identification of a given set of documents. PHI Hunter removes PHI from free-form text. It is a set of rules to identify and remove patterns in text. PHI Hunter was applied to 473 Department of Veterans Affairs (VA) text documents randomly drawn from a research corpus stored as unstructured text in VA files. PHI Hunter performed well with PHI in the form of identification numbers such as Social Security numbers, phone numbers, and medical record numbers. The most commonly missed PHI items were names and locations. Incorrect removal of information occurred with text that looked like identification numbers. PHI Hunter fills a niche role that is related to but not equal to the role of de-identification tools. It gives research staff a tool to reasonably increase patient privacy. It performs well for highly sensitive PHI categories that are rarely used in research, but still shows possible areas for improvement. More development for patterns of text and linked demographic tables from electronic health records (EHRs) would improve the program so that more precise identifiable information can be removed. PHI Hunter is an accessible tool that can flexibly remove PHI not needed for research. If it can be tailored to the specific data set via linked demographic tables, its performance will improve in each new document set.
Jednoróg, Katarzyna; Bola, Łukasz; Mostowski, Piotr; Szwed, Marcin; Boguszewski, Paweł M; Marchewka, Artur; Rutkowski, Paweł
In several countries natural sign languages were considered inadequate for education. Instead, new sign-supported systems were created, based on the belief that spoken/written language is grammatically superior. One such system called SJM (system językowo-migowy) preserves the grammatical and lexical structure of spoken Polish and since 1960s has been extensively employed in schools and on TV. Nevertheless, the Deaf community avoids using SJM for everyday communication, its preferred language being PJM (polski język migowy), a natural sign language, structurally and grammatically independent of spoken Polish and featuring classifier constructions (CCs). Here, for the first time, we compare, with fMRI method, the neural bases of natural vs. devised communication systems. Deaf signers were presented with three types of signed sentences (SJM and PJM with/without CCs). Consistent with previous findings, PJM with CCs compared to either SJM or PJM without CCs recruited the parietal lobes. The reverse comparison revealed activation in the anterior temporal lobes, suggesting increased semantic combinatory processes in lexical sign comprehension. Finally, PJM compared with SJM engaged left posterior superior temporal gyrus and anterior temporal lobe, areas crucial for sentence-level speech comprehension. We suggest that activity in these two areas reflects greater processing efficiency for naturally evolved sign language. Copyright © 2015 Elsevier Ltd. All rights reserved.
Rojas Barahona , Lina Maria; Quaglini , Silvana; Stefanelli , Mario
International audience; The prospective home-care management will probably of- fer intelligent conversational assistants for supporting patients at home through natural language interfaces. Homecare assistance in natural lan- guage, HomeNL, is a proof-of-concept dialogue system for the manage- ment of patients with hypertension. It follows up a conversation with a patient in which the patient is able to take the initiative. HomeNL pro- cesses natural language, makes an internal representation...
The Cross-Lingual Information Retrieval system (CLIR) or Multilingual Information Retrieval (MIR) has become the key issue in electronic documents management systems in a multinational environment. We propose here a multilingual information retrieval system consisting of a morpho-syntactic analyser, a transfer system from source language to target language and an information retrieval system. A thorough investigation into the system architecture and the transfer mechanisms is proposed in that report, using two different performance evaluation methods. (author) [fr
Li, Peggy; Dunham, Yarrow; Carey, Susan
Shown an entity (e.g., a plastic whisk) labeled by a novel noun in neutral syntax, speakers of Japanese, a classifier language, are more likely to assume the noun refers to the substance (plastic) than are speakers of English, a count/mass language, who are instead more likely to assume it refers to the object kind [whisk; Imai, M., & Gentner, D.…
Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R
Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.
Full Text Available Natural Language Processing is one of the most developing fields in research area. In most of the applications related to the Natural Language Processing findings of the Morphological Analysis and Morphological Generation can be considered very important. As morphological study is the technique to recognise a word and its output can be used on later on stages .Keeping in view this importance this paper describes how Morphological Analysis and Morphological Generation can be proved as an important part of various Natural Language Processing fields such as Spell checker Machine Translation etc.
Ming Che Lee
Full Text Available This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.
Chang, Jia Wei; Hsieh, Tung Cheng
This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure. PMID:24982952
Kiraz, George Anton
This book presents a tractable computational model that can cope with complex morphological operations, especially in Semitic languages, and less complex morphological systems present in Western languages. It outlines a new generalized regular rewrite rule system that uses multiple finite-state automata to cater to root-and-pattern morphology,…
Wagner, J C; Solomon, W D; Michel, P A; Juge, C; Baud, R H; Rector, A L; Scherrer, J R
Re-usable and sharable, and therefore language-independent concept models are of increasing importance in the medical domain. The GALEN project (Generalized Architecture for Languages Encyclopedias and Nomenclatures in Medicine) aims at developing language-independent concept representation systems as the foundations for the next generation of multilingual coding systems. For use within clinical applications, the content of the model has to be mapped to natural language. A so-called Multilingual Information Module (MM) establishes the link between the language-independent concept model and different natural languages. This text generation software must be versatile enough to cope at the same time with different languages and with different parts of a compositional model. It has to meet, on the one hand, the properties of the language as used in the medical domain and, on the other hand, the specific characteristics of the underlying model and its representation formalism. We propose a semantic-oriented approach to natural language generation that is based on linguistic annotations to a concept model. This approach is realized as an integral part of a Terminology Server, built around the concept model and offering different terminological services for clinical applications.
Morháč, Miroslav; Matoušek, Vladislav
In the paper we present compact library for analysis of nuclear spectra. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting. The functions can process one- and two-dimensional spectra. The software described in the paper comprises a number of conventional as well as newly developed methods needed to analyze experimental data. Program summaryProgram title: SpecAnalysLib 1.1 Catalogue identifier: AEDZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEDZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 42 154 No. of bytes in distributed program, including test data, etc.: 2 379 437 Distribution format: tar.gz Programming language: C++ Computer: Pentium 3 PC 2.4 GHz or higher, Borland C++ Builder v. 6. A precompiled Windows version is included in the distribution package Operating system: Windows 32 bit versions RAM: 10 MB Word size: 32 bits Classification: 17.6 Nature of problem: The demand for advanced highly effective experimental data analysis functions is enormous. The library package represents one approach to give the physicists the possibility to use the advanced routines simply by calling them from their own programs. SpecAnalysLib is a collection of functions for analysis of one- and two-parameter γ-ray spectra, but they can be used for other types of data as well. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting. Solution method: The algorithms of background estimation are based on Sensitive Non-linear Iterative Peak (SNIP) clipping algorithm. The smoothing algorithms are based on the convolution of the original data with several types of filters and algorithms based on discrete
Barrera, Rosalinda B.; Aleman, Magdalena
Described is a newspaper project in which elementary students report life as it was in the Middle Ages. Students are involved in a variety of language-centered activities. For example, they gather and evaluate information about medieval times and write, edit, and proofread articles for the newspaper. (RM)
Theune, Mariet; Freedman, R.; Callaway, C.
This paper describes how a language generation system that was originally designed for monologue generation, has been adapted for use in the OVIS spoken dialogue system. To meet the requirement that in a dialogue, the system’s utterances should make up a single, coherent dialogue turn, several
Describes the STAF (Science Teacher's Authoring Facility) computer-assisted instruction system developed at Leeds University (England), focusing on STAF language and major program features. Although programs for the system emphasize physical chemistry and organic spectroscopy, the system and language are general purpose and can be used in any…
We describe basic concepts and software architectures for the integration of shallow and deep (linguistics-based, semantics-oriented) natural language processing (NLP) components. The main goal of this novel, hybrid integration paradigm is improving robustness of deep processing. After an introduction to constraint-based natural language parsing, we give an overview of typical shallow processing tasks. We introduce XML standoff markup as an additional abstraction layer that eases integration ...
Service oriented chatbot systems are used to inform users in a conversational manner about a particular service or product on a website. Our research shows that current systems are time consuming to build and not very accurate or satisfying to users. We find that natural language understanding and natural language generation methods are central to creating an e�fficient and useful system. In this thesis we investigate current and past methods in this research area and place particular emph...
Marsily, G. de
Most countries agree to the necessity of burying high or medium-level wastes in geological layers situated at a few hundred meters below the ground level. The advantages and disadvantages of different types of rock such as salt, clay, granite and volcanic material are examined. Sophisticated studies are lead to determine the best geological confinement but questions arise about the time for which safety must be ensured. France has chosen 3 possible sites. These sites are geologically described in the article. The final place will be proposed after a testing phase of about 5 years in an underground facility. (A.C.)
Snefjella, Bryor; Kuperman, Victor
Existing evidence shows that more abstract mental representations are formed and more abstract language is used to characterize phenomena that are more distant from the self. Yet the precise form of the functional relationship between distance and linguistic abstractness is unknown. In four studies, we tested whether more abstract language is used in textual references to more geographically distant cities (Study 1), time points further into the past or future (Study 2), references to more socially distant people (Study 3), and references to a specific topic (Study 4). Using millions of linguistic productions from thousands of social-media users, we determined that linguistic concreteness is a curvilinear function of the logarithm of distance, and we discuss psychological underpinnings of the mathematical properties of this relationship. We also demonstrated that gradient curvilinear effects of geographic and temporal distance on concreteness are nearly identical, which suggests uniformity in representation of abstractness along multiple dimensions. © The Author(s) 2015.
Botha, Jan A.; Pitler, Emily; Ma, Ji; Bakalov, Anton; Salcianu, Alex; Weiss, David; McDonald, Ryan; Petrov, Slav
We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory...
Theune, Mariet; Freedman, R.; Callaway, C.
This paper describes how a language generation system that was originally designed for monologue generation, has been adapted for use in the OVIS spoken dialogue system. To meet the requirement that in a dialogue, the system’s utterances should make up a single, coherent dialogue turn, several modifications had to be made to the system. The paper also discusses the influence of dialogue context on information status, and its consequences for the generation of referring expressions and accentu...
Pon-Barry, Heather Roberta
The ﬁeld of spoken language processing is concerned with creating computer programs that can understand human speech and produce human-like speech. Regarding the problem of understanding human speech, there is currently growing interest in moving beyond speech recognition (the task of transcribing the words in an audio stream) and towards machine listening—interpreting the full spectrum of information in an audio stream. One part of machine listening, the problem that this thesis focuses on, ...
Full Text Available Autism spectrum disorders (ASD are pervasive neurodevelopmental disorders involving a number of deficits to linguistic cognition. The gap between genetics and the pathophysiology of ASD remains open, in particular regarding its distinctive linguistic profile. The goal of this paper is to attempt to bridge this gap, focusing on how the autistic brain processes language, particularly through the perspective of brain rhythms. Due to the phenomenon of pleiotropy, which may take some decades to overcome, we believe that studies of brain rhythms, which are not faced with problems of this scale, may constitute a more tractable route to interpreting language deficits in ASD and eventually other neurocognitive disorders. Building on recent attempts to link neural oscillations to certain computational primitives of language, we show that interpreting language deficits in ASD as oscillopathic traits is a potentially fruitful way to construct successful endophenotypes of this condition. Additionally, we will show that candidate genes for ASD are overrepresented among the genes that played a role in the evolution of language. These genes include (and are related to genes involved in brain rhythmicity. We hope that the type of steps taken here will additionally lead to a better understanding of the comorbidity, heterogeneity, and variability of ASD, and may help achieve a better treatment of the affected populations.
Emmeche, Claus; Hoffmeyer, Jesper Normann
be of considerable value, not only heuristically, but in order to comprehend the irreducible nature of living organisms. In arguing for a semiotic perspective on living nature, it makes a marked difference whether the departure is made from the tradition of F. de Saussure´s structural linguistics or from...
Harispe, Sébastien; Janaqi, Stefan
Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances def
Canfield, K.; Bray, B.; Huff, S.; Warner, H.
We describe a prototype system for semi-automatic database capture of free-text echocardiography reports. The system is very simple and uses a Unified Medical Language System compatible architecture. We use this system and a large body of texts to create a patient database and develop a comprehensive hierarchical dictionary for echocardiography.
Barker-Plummer, Dave; Dale, Robert; Cox, Richard; Romanczuk, Alex
We have assembled a large corpus of student submissions to an automatic grading system, where the subject matter involves the translation of natural language sentences into propositional logic. Of the 2.3 million translation instances in the corpus, 286,000 (approximately 12%) are categorized as being in error. We want to understand the nature of…
Do, Bao H; Wu, Andrew; Biswal, Sandip; Kamaya, Aya; Rubin, Daniel L
Storing and retrieving radiology cases is an important activity for education and clinical research, but this process can be time-consuming. In the process of structuring reports and images into organized teaching files, incidental pathologic conditions not pertinent to the primary teaching point can be omitted, as when a user saves images of an aortic dissection case but disregards the incidental osteoid osteoma. An alternate strategy for identifying teaching cases is text search of reports in radiology information systems (RIS), but retrieved reports are unstructured, teaching-related content is not highlighted, and patient identifying information is not removed. Furthermore, searching unstructured reports requires sophisticated retrieval methods to achieve useful results. An open-source, RadLex(®)-compatible teaching file solution called RADTF, which uses natural language processing (NLP) methods to process radiology reports, was developed to create a searchable teaching resource from the RIS and the picture archiving and communication system (PACS). The NLP system extracts and de-identifies teaching-relevant statements from full reports to generate a stand-alone database, thus converting existing RIS archives into an on-demand source of teaching material. Using RADTF, the authors generated a semantic search-enabled, Web-based radiology archive containing over 700,000 cases with millions of images. RADTF combines a compact representation of the teaching-relevant content in radiology reports and a versatile search engine with the scale of the entire RIS-PACS collection of case material. ©RSNA, 2010
Daltrozzo, Jerome; Emerson, Samantha N; Deocampo, Joanne; Singh, Sonia; Freggens, Marjorie; Branum-Martin, Lee; Conway, Christopher M
Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities. Copyright © 2017 Elsevier Inc. All rights reserved.
Bisikalo Oleg V.
Full Text Available The task of evaluating uncertainty in the measurement of sense in natural language constructions (NLCs was researched through formalization of the notions of the language image, formalization of artificial cognitive systems (ACSs and the formalization of units of meaning. The method for measuring the sense of natural language constructions incorporated fuzzy relations of meaning, which ensures that information about the links between lemmas of the text is taken into account, permitting the evaluation of two types of measurement uncertainty of sense characteristics. Using developed applications programs, experiments were conducted to investigate the proposed method to tackle the identification of informative characteristics of text. The experiments resulted in dependencies of parameters being obtained in order to utilise the Pareto distribution law to define relations between lemmas, analysis of which permits the identification of exponents of an average number of connections of the language image as the most informative characteristics of text.
Clody, Michael C
The essay argues that Francis Bacon's considerations of parables and cryptography reflect larger interpretative concerns of his natural philosophic project. Bacon describes nature as having a language distinct from those of God and man, and, in so doing, establishes a central problem of his natural philosophy—namely, how can the language of nature be accessed through scientific representation? Ultimately, Bacon's solution relies on a theory of differential and duplicitous signs that conceal within them the hidden voice of nature, which is best recognized in the natural forms of efficient causality. The "alphabet of nature"—those tables of natural occurrences—consequently plays a central role in his program, as it renders nature's language susceptible to a process and decryption that mirrors the model of the bilateral cipher. It is argued that while the writing of Bacon's natural philosophy strives for literality, its investigative process preserves a space for alterity within scientific representation, that is made accessible to those with the interpretative key.
Heinrich, Stefan; Wermter, Stefan
For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations.
Liu, Haitao; Xu, Chunshan; Liang, Junying
Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages.
Liu, Haitao; Xu, Chunshan; Liang, Junying
Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages. Copyright © 2017. Published by Elsevier B.V.
Groth, P.T.; Gil, Y
Scientists increasingly use workflows to represent and share their computational experiments. Because of their declarative nature, focus on pre-existing component composition and the availability of visual editors, workflows provide a valuable start for creating user-friendly environments for end
how a Concept specializes its subsumer. |C|ANIMAL. |C|PLANT. |C(PERSON, and |C| UNICORN are natural kinds, and so will need a PrimitiveClass. As...build this proof, we must build a proof of p x (p X n) steps. The size of the proofs grows exponentially with the depth of nesting This :s clearly
fundamental to knowledge management problems. In [Wijaya13] presented a novel approach to this ontology alignment problem that employs a very large natural...to them. This report is the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance...S / ALEKSEY PANASYUK MICHAEL J. WESSING Work Unit Manager Deputy Chief, Information Intelligence Systems & Analysis Division Information
Bender, Emily M
Many NLP tasks have at their core a subtask of extracting the dependencies-who did what to whom-from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual a
Full Text Available We propose a stochastic model for the number of different words in a given database which incorporates the dependence on the database size and historical changes. The main feature of our model is the existence of two different classes of words: (i a finite number of core words, which have higher frequency and do not affect the probability of a new word to be used, and (ii the remaining virtually infinite number of noncore words, which have lower frequency and, once used, reduce the probability of a new word to be used in the future. Our model relies on a careful analysis of the Google Ngram database of books published in the last centuries, and its main consequence is the generalization of Zipf’s and Heaps’ law to two-scaling regimes. We confirm that these generalizations yield the best simple description of the data among generic descriptive models and that the two free parameters depend only on the language but not on the database. From the point of view of our model, the main change on historical time scales is the composition of the specific words included in the finite list of core words, which we observe to decay exponentially in time with a rate of approximately 30 words per year for English.
Full Text Available Intelligent interface, to enhance efficient interactions between user and databases, is the need of the database applications. Databases must be intelligent enough to make the accessibility faster. However, not every user familiar with the Structured Query Language (SQL queries as they may not aware of structure of the database and they thus require to learn SQL. So, non-expert users need a system to interact with relational databases in their natural language such as English. For this, Database Management System (DBMS must have an ability to understand Natural Language (NL. In this research, an intelligent interface is developed using semantic matching technique which translates natural language query to SQL using set of production rules and data dictionary. The data dictionary consists of semantics sets for relations and attributes. A series of steps like lower case conversion, tokenization, speech tagging, database element and SQL element extraction is used to convert Natural Language Query (NLQ to SQL Query. The transformed query is executed and the results are obtained by the user. Intelligent Interface is the need of database applications to enhance efficient interaction between user and DBMS.
Feng, Qiangze; Qi, Hongwei; Fukushima, Toshikazu
Information services accessed via mobile phones provide information directly relevant to subscribers’ daily lives and are an area of dynamic market growth worldwide. Although many information services are currently offered by mobile operators, many of the existing solutions require a unique gateway for each service, and it is inconvenient for users to have to remember a large number of such gateways. Furthermore, the Short Message Service (SMS) is very popular in China and Chinese users would prefer to access these services in natural language via SMS. This chapter describes a Natural Language Based Service Selection System (NL3S) for use with a large number of mobile information services. The system can accept user queries in natural language and navigate it to the required service. Since it is difficult for existing methods to achieve high accuracy and high coverage and anticipate which other services a user might want to query, the NL3S is developed based on a Multi-service Ontology (MO) and Multi-service Query Language (MQL). The MO and MQL provide semantic and linguistic knowledge, respectively, to facilitate service selection for a user query and to provide adaptive service recommendations. Experiments show that the NL3S can achieve 75-95% accuracies and 85-95% satisfactions for processing various styles of natural language queries. A trial involving navigation of 30 different mobile services shows that the NL3S can provide a viable commercial solution for mobile operators.
MILROY, Lesley. Observing and Analysing Natural Language: A Critical Account of Sociolinguistic Method. Oxford: Basil Blackwell, 1987. 230pp. MILROY, Lesley. Observing and Analysing Natural Language: A Critical Account of Sociolinguistic Method. Oxford: Basil Blackwell, 1987. 230pp.
Iria Werlang Garcia
Full Text Available Lesley Milroy's Observing and Analysing Natural Language is a recent addition to an ever growing number of publications in the field of Sociolinguistics. It carries the weight of one of the experienced authors in the current days in the specified field and should offer basic information to both newcomers and established investigators in natural language. Lesley Milroy's Observing and Analysing Natural Language is a recent addition to an ever growing number of publications in the field of Sociolinguistics. It carries the weight of one of the experienced authors in the current days in the specified field and should offer basic information to both newcomers and established investigators in natural language.
interpretation would not be too bad if one were to believe that a frame "is intended to represent a ’ stereotypical situation’" ( , p. 48). We...natural kind-like concepts - some form of definitional structuring is necessary. The internal structure of non atomic concepts (e.g., proximate genus ...types of beer, bottles of wine, etc.; <x> need not be any sort of Onatural genus .’ For example, in Dll the definite pronoun Othem" is not meant to I
Thessen,Anne; Preciado,Jenette; Jain,Payoj; Martin,James; Palmer,Martha; Bhat,Riyaz
The cTAKES package (using the ClearTK Natural Language Processing toolkit Bethard et al. 2014, http://cleartk.github.io/cleartk/) has been successfully used to automatically read clinical notes in the medical field (Albright et al. 2013, Styler et al. 2014). It is used on a daily basis to automatically process clinical notes and extract relevant information by dozens of medical institutions. ClearEarth is a collaborative project that brings together computational linguistics and domain scient...
Topac, Vasile; Jurcau, Daniel-Alexandru; Stoicu-Tivadar, Vasile
Medical terminology appears in the natural language in multiple forms: canonical, derived or inflected form. This research presents an analysis of the form in which medical terminology appears in Romanian and English language. The sources of medical language used for the study are web pages presenting medical information for patients and other lay users. The results show that, in English, medical terminology tends to appear more in canonical form while, in the case of Romanian, it is the opposite. This paper also presents the service that was created to perform this analysis. This tool is available for the general public, and it is designed to be easily extensible, allowing the addition of other languages.
Fitzpatrick, A.Liam; /Boston U.; Kaplan, Jared; /SLAC; Penedones, Joao; /Perimeter Inst. Theor. Phys.; Raju, Suvrat; /Harish-Chandra Res. Inst.; van Rees, Balt C.; /YITP, Stony Brook
We provide dramatic evidence that 'Mellin space' is the natural home for correlation functions in CFTs with weakly coupled bulk duals. In Mellin space, CFT correlators have poles corresponding to an OPE decomposition into 'left' and 'right' sub-correlators, in direct analogy with the factorization channels of scattering amplitudes. In the regime where these correlators can be computed by tree level Witten diagrams in AdS, we derive an explicit formula for the residues of Mellin amplitudes at the corresponding factorization poles, and we use the conformal Casimir to show that these amplitudes obey algebraic finite difference equations. By analyzing the recursive structure of our factorization formula we obtain simple diagrammatic rules for the construction of Mellin amplitudes corresponding to tree-level Witten diagrams in any bulk scalar theory. We prove the diagrammatic rules using our finite difference equations. Finally, we show that our factorization formula and our diagrammatic rules morph into the flat space S-Matrix of the bulk theory, reproducing the usual Feynman rules, when we take the flat space limit of AdS/CFT. Throughout we emphasize a deep analogy with the properties of flat space scattering amplitudes in momentum space, which suggests that the Mellin amplitude may provide a holographic definition of the flat space S-Matrix.
Leung, Constant; Scarino, Angela
Transformations associated with the increasing speed, scale, and complexity of mobilities, together with the information technology revolution, have changed the demography of most countries of the world and brought about accompanying social, cultural, and economic shifts (Heugh, 2013). This complex diversity has changed the very nature of…
Dawson, Caitlin; Aalto, Daniel; Šimko, Juraj; Vainio, Martti; Tervaniemi, Mari
Musical experiences and native language are both known to affect auditory processing. The present work aims to disentangle the influences of native language phonology and musicality on behavioral and subcortical sound feature processing in a population of musically diverse Finnish speakers as well as to investigate the specificity of enhancement from musical training. Finnish speakers are highly sensitive to duration cues since in Finnish, vowel and consonant duration determine word meaning. Using a correlational approach with a set of behavioral sound feature discrimination tasks, brainstem recordings, and a musical sophistication questionnaire, we find no evidence for an association between musical sophistication and more precise duration processing in Finnish speakers either in the auditory brainstem response or in behavioral tasks, but they do show an enhanced pitch discrimination compared to Finnish speakers with less musical experience and show greater duration modulation in a complex task. These results are consistent with a ceiling effect set for certain sound features which corresponds to the phonology of the native language, leaving an opportunity for music experience-based enhancement of sound features not explicitly encoded in the language (such as pitch, which is not explicitly encoded in Finnish). Finally, the pattern of duration modulation in more musically sophisticated Finnish speakers suggests integrated feature processing for greater efficiency in a real world musical situation. These results have implications for research into the specificity of plasticity in the auditory system as well as to the effects of interaction of specific language features with musical experiences. PMID:28450829
Dawson, Caitlin; Aalto, Daniel; Šimko, Juraj; Vainio, Martti; Tervaniemi, Mari
Musical experiences and native language are both known to affect auditory processing. The present work aims to disentangle the influences of native language phonology and musicality on behavioral and subcortical sound feature processing in a population of musically diverse Finnish speakers as well as to investigate the specificity of enhancement from musical training. Finnish speakers are highly sensitive to duration cues since in Finnish, vowel and consonant duration determine word meaning. Using a correlational approach with a set of behavioral sound feature discrimination tasks, brainstem recordings, and a musical sophistication questionnaire, we find no evidence for an association between musical sophistication and more precise duration processing in Finnish speakers either in the auditory brainstem response or in behavioral tasks, but they do show an enhanced pitch discrimination compared to Finnish speakers with less musical experience and show greater duration modulation in a complex task. These results are consistent with a ceiling effect set for certain sound features which corresponds to the phonology of the native language, leaving an opportunity for music experience-based enhancement of sound features not explicitly encoded in the language (such as pitch, which is not explicitly encoded in Finnish). Finally, the pattern of duration modulation in more musically sophisticated Finnish speakers suggests integrated feature processing for greater efficiency in a real world musical situation. These results have implications for research into the specificity of plasticity in the auditory system as well as to the effects of interaction of specific language features with musical experiences.
Nye, Benjamin D.; Graesser, Arthur C.; Hu, Xiangen
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Massaro, Dominic W
I review 2 seminal research reports published in this journal during its second decade more than a century ago. Given psychology's subdisciplines, they would not normally be reviewed together because one involves reading and the other speech perception. The small amount of interaction between these domains might have limited research and theoretical progress. In fact, the 2 early research reports revealed common processes involved in these 2 forms of language processing. Their illustration of the role of Wundt's apperceptive process in reading and speech perception anticipated descriptions of contemporary theories of pattern recognition, such as the fuzzy logical model of perception. Based on the commonalities between reading and listening, one can question why they have been viewed so differently. It is commonly believed that learning to read requires formal instruction and schooling, whereas spoken language is acquired from birth onward through natural interactions with people who talk. Most researchers and educators believe that spoken language is acquired naturally from birth onward and even prenatally. Learning to read, on the other hand, is not possible until the child has acquired spoken language, reaches school age, and receives formal instruction. If an appropriate form of written text is made available early in a child's life, however, the current hypothesis is that reading will also be learned inductively and emerge naturally, with no significant negative consequences. If this proposal is true, it should soon be possible to create an interactive system, Technology Assisted Reading Acquisition, to allow children to acquire literacy naturally.
Higginbotham, D Jeffery; Lesher, Gregory W; Moulton, Bryan J; Roark, Brian
Significant progress has been made in the application of natural language processing (NLP) to augmentative and alternative communication (AAC), particularly in the areas of interface design and word prediction. This article will survey the current state-of-the-science of NLP in AAC and discuss its future applications for the development of next generation of AAC technology.
Krahmer, E.; Krahmer, E.; Theune, Mariet
We are pleased to present the Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009). ENLG 2009 was held in Athens, Greece, as a workshop at the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009). Following our call, we
A theoretical discussion is offered on whether the subjunctive in the Romance languages is by nature thematic, as suggested in previous studies. English and Spanish samples are used to test the hypothesis; one conclusion is that the subjunctive seems to offer speaker-related information and may express the intensity of the speaker's involvement.…
Laski, Karen E.; And Others
Parents of four nonverbal and four echolalic autistic children, aged five-nine, were trained to increase their children's speech by using the Natural Language Paradigm. Following training, parents increased the frequency with which they required their children to speak, and children increased the frequency of their verbalizations in three…
Stoianov, [No Value; Nerbonne, J; Bouma, H; Coppen, PA; vanHalteren, H; Teunissen, L
Simple Recurrent Networks (SRN) are Neural Network (connectionist) models able to process natural language. Phonotactics concerns the order of symbols in words. We continued an earlier unsuccessful trial to model the phonotactics of Dutch words with SRNs. In order to overcome the previously reported
Ingram, D. E.
The nature and development of the recently released International English Language Testing System (IELTS) instrument are described. The test is the result of a joint Australian-British project to develop a new test for use with foreign students planning to study in English-speaking countries. It is expected that the modular instrument will become…
Tierney, Patrick J.
This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool--graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a "graphical solution". Rather, it is a method to help qualitative…
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Wong, Wing-Kwong; Yin, Sheng-Kai; Yang, Chang-Zhe
This paper presents a tool for drawing dynamic geometric figures by understanding the texts of geometry problems. With the tool, teachers and students can construct dynamic geometric figures on a web page by inputting a geometry problem in natural language. First we need to build the knowledge base for understanding geometry problems. With the…
Kyle, Kristopher; Crossley, Scott A.; McNamara, Danielle S.
This study explores the construct validity of speaking tasks included in the TOEFL iBT (e.g., integrated and independent speaking tasks). Specifically, advanced natural language processing (NLP) tools, MANOVA difference statistics, and discriminant function analyses (DFA) are used to assess the degree to which and in what ways responses to these…
Christina Siu-Dschu Fan
Full Text Available In tonal languages, such as Mandarin Chinese, the pitch contour of vowels discriminates lexical meaning, which is not the case in non-tonal languages such as German. Recent data provide evidence that pitch processing is influenced by language experience. However, there are still many open questions concerning the representation of such phonological and language-related differences at the level of the auditory cortex (AC. Using magnetoencephalography (MEG, we recorded transient and sustained auditory evoked fields (AEF in native Chinese and German speakers to investigate language related phonological and semantic aspects in the processing of acoustic stimuli. AEF were elicited by spoken meaningful and meaningless syllables, by vowels, and by a French horn tone. Speech sounds were recorded from a native speaker and showed frequency-modulations according to the pitch-contours of Mandarin. The sustained field (SF evoked by natural speech signals was significantly larger for Chinese than for German listeners. In contrast, the SF elicited by a horn tone was not significantly different between groups. Furthermore, the SF of Chinese subjects was larger when evoked by meaningful syllables compared to meaningless ones, but there was no significant difference regarding whether vowels were part of the Chinese phonological system or not. Moreover, the N100m gave subtle but clear evidence that for Chinese listeners other factors than purely physical properties play a role in processing meaningful signals. These findings show that the N100 and the SF generated in Heschl's gyrus are influenced by language experience, which suggests that AC activity related to specific pitch contours of vowels is influenced in a top-down fashion by higher, language related areas. Such interactions are in line with anatomical findings and neuroimaging data, as well as with the dual-stream model of language of Hickok and Poeppel that highlights the close and reciprocal interaction
Fan, Christina Siu-Dschu; Zhu, Xingyu; Dosch, Hans Günter; von Stutterheim, Christiane; Rupp, André
In tonal languages, such as Mandarin Chinese, the pitch contour of vowels discriminates lexical meaning, which is not the case in non-tonal languages such as German. Recent data provide evidence that pitch processing is influenced by language experience. However, there are still many open questions concerning the representation of such phonological and language-related differences at the level of the auditory cortex (AC). Using magnetoencephalography (MEG), we recorded transient and sustained auditory evoked fields (AEF) in native Chinese and German speakers to investigate language related phonological and semantic aspects in the processing of acoustic stimuli. AEF were elicited by spoken meaningful and meaningless syllables, by vowels, and by a French horn tone. Speech sounds were recorded from a native speaker and showed frequency-modulations according to the pitch-contours of Mandarin. The sustained field (SF) evoked by natural speech signals was significantly larger for Chinese than for German listeners. In contrast, the SF elicited by a horn tone was not significantly different between groups. Furthermore, the SF of Chinese subjects was larger when evoked by meaningful syllables compared to meaningless ones, but there was no significant difference regarding whether vowels were part of the Chinese phonological system or not. Moreover, the N100m gave subtle but clear evidence that for Chinese listeners other factors than purely physical properties play a role in processing meaningful signals. These findings show that the N100 and the SF generated in Heschl's gyrus are influenced by language experience, which suggests that AC activity related to specific pitch contours of vowels is influenced in a top-down fashion by higher, language related areas. Such interactions are in line with anatomical findings and neuroimaging data, as well as with the dual-stream model of language of Hickok and Poeppel that highlights the close and reciprocal interaction between
Mathematics and the Laws of Nature, Revised Edition describes the evolution of the idea that nature can be described in the language of mathematics. Colorful chapters explore the earliest attempts to apply deductive methods to the study of the natural world. This revised resource goes on to examine the development of classical conservation laws, including the conservation of momentum, the conservation of mass, and the conservation of energy. Chapters have been updated and revised to reflect recent information, including the mathematical pioneers who introduced new ideas about what it meant to
R. Bruce Hull; David P. Robertson; Angelina Kendra
This study is intended to serve as an explicit and specific example of the social construction of nature. It is motivated by the need to develop a more sophisticated language for a critical public dialogue about society's relationship with nature. We conducted a case study of environmental discourse in one local population in hopes of better understanding how a...
Sanden, Guro Refsum
Purpose: – The purpose of this paper is to analyse the consequences of globalisation in the area of corporate communication, and investigate how language may be managed as a strategic resource. Design/methodology/approach: – A review of previous studies on the effects of globalisation on corporate...... communication and the implications of language management initiatives in international business. Findings: – Efficient language management can turn language into a strategic resource. Language needs analyses, i.e. linguistic auditing/language check-ups, can be used to determine the language situation...... of a company. Language policies and/or strategies can be used to regulate a company’s internal modes of communication. Language management tools can be deployed to address existing and expected language needs. Continuous feedback from the front line ensures strategic learning and reduces the risk of suboptimal...
Jackendoff, Ray; Pinker, Steven
In a continuation of the conversation with Fitch, Chomsky, and Hauser on the evolution of language, we examine their defense of the claim that the uniquely human, language-specific part of the language faculty (the ''narrow language faculty'') consists only of recursion, and that this part cannot be considered an adaptation to communication. We…
This paper examines whether the sophistication of market investors influences management's strategy on discretionary accounting choice, and thus changes the persistence of discretionary accruals. The results show that the persistence of discretionary accruals for firms face with naive investors is lower than that for firms face with sophisticated investors. The results also demonstrate that sophisticated investors indeed incorporate the implications of current earnings components into future ...
Full Text Available Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.
Rassinoux, Anne-Marie; Baud, Robert H; Rodrigues, Jean-Marie; Lovis, Christian; Geissbühler, Antoine
The importance of clinical communication between providers, consumers and others, as well as the requisite for computer interoperability, strengthens the need for sharing common accepted terminologies. Under the directives of the World Health Organization (WHO), an approach is currently being conducted in Australia to adopt a standardized terminology for medical procedures that is intended to become an international reference. In order to achieve such a standard, a collaborative approach is adopted, in line with the successful experiment conducted for the development of the new French coding system CCAM. Different coding centres are involved in setting up a semantic representation of each term using a formal ontological structure expressed through a logic-based representation language. From this language-independent representation, multilingual natural language generation (NLG) is performed to produce noun phrases in various languages that are further compared for consistency with the original terms. Outcomes are presented for the assessment of the International Classification of Health Interventions (ICHI) and its translation into Portuguese. The initial results clearly emphasize the feasibility and cost-effectiveness of the proposed method for handling both a different classification and an additional language. NLG tools, based on ontology driven semantic representation, facilitate the discovery of ambiguous and inconsistent terms, and, as such, should be promoted for establishing coherent international terminologies.
Sharma, Vivekanand; Law, Wayne; Balick, Michael J; Sarkar, Indra Neil
The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement.
Molina, Martin; Sanchez-Soriano, Javier; Corcho, Oscar
Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions.
Min, Yul Ha; Park, Hyeoun-Ae; Jeon, Eunjoo; Lee, Joo Yun; Jo, Soo Jung
The purpose of this study was to develop an ontology model to generate nursing narratives as natural as human language from the entity-attribute-value triplets of a detailed clinical model using natural language generation technology. The model was based on the types of information and documentation time of the information along the nursing process. The typesof information are data characterizing the patient status, inferences made by the nurse from the patient data, and nursing actions selected by the nurse to change the patient status. This information was linked to the nursing process based on the time of documentation. We describe a case study illustrating the application of this model in an acute-care setting. The proposed model provides a strategy for designing an electronic nursing record system.
Hunter, James; Freer, Yvonne; Gatt, Albert; Reiter, Ehud; Sripada, Somayajulu; Sykes, Cindy; Westwater, Dave
The BT-Nurse system uses data-to-text technology to automatically generate a natural language nursing shift summary in a neonatal intensive care unit (NICU). The summary is solely based on data held in an electronic patient record system, no additional data-entry is required. BT-Nurse was tested for two months in the Royal Infirmary of Edinburgh NICU. Nurses were asked to rate the understandability, accuracy, and helpfulness of the computer-generated summaries; they were also asked for free-text comments about the summaries. The nurses found the majority of the summaries to be understandable, accurate, and helpful (pgenerated summaries. In conclusion, natural language NICU shift summaries can be automatically generated from an electronic patient record, but our proof-of-concept software needs considerable additional development work before it can be deployed.
Full Text Available Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods and their impact in the generation of sensor descriptions.
Stickland, Michael G.; Conrad, Gregory N.; Eaton, Shelley M.
Natural language processing-based knowledge management software, traditionally developed for security organizations, is now becoming commercially available. An informal survey was conducted to discover and examine current NLP and related technologies and potential applications for information retrieval, information extraction, summarization, categorization, terminology management, link analysis, and visualization for possible implementation at Sandia National Laboratories. This report documents our current understanding of the technologies, lists software vendors and their products, and identifies potential applications of these technologies.
Weng, Wei-Hung; Wagholikar, Kavishwar B.; McCray, Alexa T.; Szolovits, Peter; Chueh, Henry C.
Background The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. Methods We constructed the pipeline using the clinical ...
conversational agent with information exchange disabled until the end of the experiment run. The meaning of the indicator in the top- right of the agent... Human Computer Collaboration at the Edge: Enhancing Collective Situation Understanding with Controlled Natural Language Alun Preece∗, William...email: PreeceAD@cardiff.ac.uk †Emerging Technology Services, IBM United Kingdom Ltd, Hursley Park, Winchester, UK ‡US Army Research Laboratory, Human
Will, Herbert A.; Mackin, Michael A.
PC software is described which provides flexible natural language process control capability with an IBM PC or compatible machine. Hardware requirements include the PC, and suitable hardware interfaces to all controlled devices. Software required includes the Microsoft Disk Operating System (MS-DOS) operating system, a PC-based FORTRAN-77 compiler, and user-written device drivers. Instructions for use of the software are given as well as a description of an application of the system.
Compact Closed categories and Frobenius and Bi algebras have been applied to model and reason about Quantum protocols. The same constructions have also been applied to reason about natural language semantics under the name: ``categorical distributional compositional'' semantics, or in short, the ``DisCoCat'' model. This model combines the statistical vector models of word meaning with the compositional models of grammatical structure. It has been applied to natural language tasks such as disambiguation, paraphrasing and entailment of phrases and sentences. The passage from the grammatical structure to vectors is provided by a functor, similar to the Quantization functor of Quantum Field Theory. The original DisCoCat model only used compact closed categories. Later, Frobenius algebras were added to it to model long distance dependancies such as relative pronouns. Recently, bialgebras have been added to the pack to reason about quantifiers. This paper reviews these constructions and their application to natural language semantics. We go over the theory and present some of the core experimental results.
Full Text Available Compact Closed categories and Frobenius and Bi algebras have been applied to model and reason about Quantum protocols. The same constructions have also been applied to reason about natural language semantics under the name: “categorical distributional compositional” semantics, or in short, the “DisCoCat” model. This model combines the statistical vector models of word meaning with the compositional models of grammatical structure. It has been applied to natural language tasks such as disambiguation, paraphrasing and entailment of phrases and sentences. The passage from the grammatical structure to vectors is provided by a functor, similar to the Quantization functor of Quantum Field Theory. The original DisCoCat model only used compact closed categories. Later, Frobenius algebras were added to it to model long distance dependancies such as relative pronouns. Recently, bialgebras have been added to the pack to reason about quantifiers. This paper reviews these constructions and their application to natural language semantics. We go over the theory and present some of the core experimental results.
Smith, Hyrum; Finke, Michael S.; Huston, Sandra J.
The influence of a financial sophistication scale on adjustable-rate mortgage (ARM) borrowing is explored. Descriptive statistics and regression analysis using recent data from the Survey of Consumer Finances reveal that ARM borrowing is driven by both the least and most financially sophisticated households but for different reasons. Less…
Naranjo Gil, David
Organizations are designing more sophisticated accounting information systems to meet the strategic goals and enhance their performance. This study examines the effect of accounting information system design on the performance of organizations pursuing different strategic priorities. The alignment between sophisticated accounting information systems and organizational strategy is analyzed. The enabling effect of the accounting information system on performance is also examined. Relationships ...
Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci; Luigi Montrucchio
We study the interplay of probabilistic sophistication, second order stochastic dominance, and uncertainty aversion, three fundamental notions in choice under uncertainty. In particular, our main result, Theorem 2, characterizes uncertainty averse preferences that satisfy second order stochastic dominance, as well as uncertainty averse preferences that are probabilistically sophisticated.
Jarratt, Susan C.
Reviews the history of intellectual views on the Greek sophists in three phases: (1) their disparagement by Plato and Aristotle as the morally disgraceful "other"; (2) nineteenth century British positivists' reappraisal of these relativists as ethically and scientifically superior; and (3) twentieth century versions of the sophists as…
Surveys developments in language revitalization and language death. Focusing on indigenous languages, discusses the role and nature of appropriate linguistic documentation, possibilities for bilingual education, and methods of promoting oral fluency and intergenerational transmission in affected languages. (Author/VWL)
One dimension of early Canadian education is the attempt of the government to use the education system as an assimilative tool to integrate the First Nations and Me´tis people into Euro-Canadian society. Despite these attempts, many First Nations and Me´tis people retained their culture and their indigenous language. Few science educators have examined First Nations and Western scientific worldviews and the impact they may have on science learning. This study explored the views some First Nations (Cree) and Euro-Canadian Grade-7-level students in Manitoba had about the nature of science. Both qualitative (open-ended questions and interviews) and quantitative (a Likert-scale questionnaire) instruments were used to explore student views. A central hypothesis to this research programme is the possibility that the different world-views of two student populations, Cree and Euro-Canadian, are likely to influence their perceptions of science. This preliminary study explored a range of methodologies to probe the perceptions of the nature of science in these two student populations. It was found that the two cultural groups differed significantly between some of the tenets in a Nature of Scientific Knowledge Scale (NSKS). Cree students significantly differed from Euro-Canadian students on the developmental, testable and unified tenets of the nature of scientific knowledge scale. No significant differences were found in NSKS scores between language groups (Cree students who speak English in the home and those who speak English and Cree or Cree only). The differences found between language groups were primarily in the open-ended questions where preformulated responses were absent. Interviews about critical incidents provided more detailed accounts of the Cree students' perception of the nature of science. The implications of the findings of this study are discussed in relation to the challenges related to research methodology, further areas for investigation, science
Woo, Chong Woo; Evens, Martha W; Freedman, Reva; Glass, Michael; Shim, Leem Seop; Zhang, Yuemei; Zhou, Yujian; Michael, Joel
The objective of this research was to build an intelligent tutoring system capable of carrying on a natural language dialogue with a student who is solving a problem in physiology. Previous experiments have shown that students need practice in qualitative causal reasoning to internalize new knowledge and to apply it effectively and that they learn by putting their ideas into words. Analysis of a corpus of 75 hour-long tutoring sessions carried on in keyboard-to-keyboard style by two professors of physiology at Rush Medical College tutoring first-year medical students provided the rules used in tutoring strategies and tactics, parsing, and text generation. The system presents the student with a perturbation to the blood pressure, asks for qualitative predictions of the changes produced in seven important cardiovascular variables, and then launches a dialogue to correct any errors and to probe for possible misconceptions. The natural language understanding component uses a cascade of finite-state machines. The generation is based on lexical functional grammar. Results of experiments with pretests and posttests have shown that using the system for an hour produces significant learning gains and also that even this brief use improves the student's ability to solve problems more then reading textual material on the topic. Student surveys tell us that students like the system and feel that they learn from it. The system is now in regular use in the first-year physiology course at Rush Medical College. We conclude that the CIRCSIM-Tutor system demonstrates that intelligent tutoring systems can implement effective natural language dialogue with current language technology.
Pahisa-Solé, Joan; Herrera-Joancomartí, Jordi
In this article, we describe a compansion system that transforms the telegraphic language that comes from the use of pictogram-based augmentative and alternative communication (AAC) into natural language. The system was tested with four participants with severe cerebral palsy and ranging degrees of linguistic competence and intellectual disabilities. Participants had used pictogram-based AAC at least for the past 30 years each and presented a stable linguistic profile. During tests, which consisted of a total of 40 sessions, participants were able to learn new linguistic skills, such as the use of basic verb tenses, while using the compansion system, which proved a source of motivation. The system can be adapted to the linguistic competence of each person and required no learning curve during tests when none of its special features, like gender, number, verb tense, or sentence type modifiers, were used. Furthermore, qualitative and quantitative results showed a mean communication rate increase of 41.59%, compared to the same communication device without the compansion system, and an overall improvement in the communication experience when the output is in natural language. Tests were conducted in Catalan and Spanish.
Mazlack, L.J.; Paz, N.M.
Newspaper cartoons can graphically display the result of ambiguity in human speech; the result can be unexpected and funny. Likewise, computer analysis of natural language statements also needs to successfully resolve ambiguous situations. Computer techniques already developed use restricted world knowledge in resolving ambiguous language use. This paper illustrates how these techniques can be used in resolving ambiguous situations arising in cartoons. 8 references.
Erickson, Sarah J.; Montague, Erica Q.; Maclean, Peggy C.; Bancroft, Mary E.; Lowe, Jean R.
Children born very low birth weight (development of self-regulation and effective functional skills, and play serves as an important avenue of early intervention. The current study investigated associations between maternal flexibility and toddler play sophistication in Caucasian, Spanish speaking Hispanic, English speaking Hispanic, and Native American toddlers (18-22 months adjusted age) in a cross-sectional cohort of 73 toddlers born VLBW and their mothers. We found that the association between maternal flexibility and toddler play sophistication differed by ethnicity (F(3,65) = 3.34, p = .02). In particular, Spanish speaking Hispanic dyads evidenced a significant positive association between maternal flexibility and play sophistication of medium effect size. Results for Native Americans were parallel to those of Spanish speaking Hispanic dyads: the relationship between flexibility and play sophistication was positive and of small-medium effect size. Findings indicate that for Caucasians and English speaking Hispanics, flexibility evidenced a non-significant (negative and small effect size) association with toddler play sophistication. Significant follow-up contrasts revealed that the associations for Caucasian and English speaking Hispanic dyads were significantly different from those of the other two ethnic groups. Results remained unchanged after adjusting for the amount of maternal language, an index of maternal engagement and stimulation; and after adjusting for birth weight, gestational age, gender, test age, cognitive ability, as well maternal age, education, and income. Our results provide preliminary evidence that ethnicity and acculturation may mediate the association between maternal interactive behavior such as flexibility and toddler developmental outcomes, as indexed by play sophistication. Addressing these association differences is particularly important in children born VLBW because interventions targeting parent interaction strategies such as
Roberto Pirrone; Giuseppe Russo; Vincenzo Cannella; Daniele Peri
Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and sp...
Full Text Available The semantic web extends the current World Wide Web by adding facilities for the machine understood description of meaning. The ontology based search model is used to enhance efficiency and accuracy of information retrieval. Ontology is the core technology for the semantic web and this mechanism for representing formal and shared domain descriptions. In this paper, we proposed ontology based meaningful search using semantic web and Natural Language Processing (NLP techniques in the educational domain. First we build the educational ontology then we present the semantic search system. The search model consisting three parts which are embedding spell-check, finding synonyms using WordNet API and querying ontology using SPARQL language. The results are both sensitive to spell check and synonymous context. This paper provides more accurate results and the complete details for the selected field in a single page.
Famed for his collection of drawings of naturalia and his thoughts on the relationship between painting and natural knowledge, it now appears that the Bolognese naturalist Ulisse Aldrovandi (1522-1605) also pondered specifically color and pigments, compiling not only lists and diagrams of color terms but also a full-length unpublished manuscript entitled De coloribus or Trattato dei colori. Introducing these writings for the first time, this article portrays a scholar not so much interested in the materiality of pigment production, as in the cultural history of hues. It argues that these writings constituted an effort to build a language of color, in the sense both of a standard nomenclature of hues and of a lexicon, a dictionary of their denotations and connotations as documented in the literature of ancients and moderns. This language would serve the naturalist in his artistic patronage and his natural historical studies, where color was considered one of the most reliable signs for the correct identification of specimens, and a guarantee of accuracy in their illustration. Far from being an exception, Aldrovandi's 'color sensibility'spoke of that of his university-educated nature-loving peers.
use of modern rhetorical theories but analyses the letter in terms of the clas- ..... If a critical reader would have had the traditional anti-sophistic arsenal ..... pressions and that 'rhetoric' is mainly a matter of communicating these thoughts.
Full Text Available Nonprice competition is increasingly important in world food markets. Recently, the expression ‘export sophistication’ has been introduced in the economic literature to refer to a wide set of attributes that increase product value. An index has been proposed to measure sophistication in an indirect way through the per capita GDP of exporting countries (Lall et al., 2006; Haussmann et al., 2007.The paper applies the sophistication measure to the Italian food export sector, moving from an analysis of trends and performance of Italian food exports. An original way to disentangle different components in the temporal variation of the sophistication index is also proposed.Results show that the sophistication index offers original insights on recent trends in world food exports and with respect to Italian core food exports.
This volume deals with the computational application of systemic functional grammar (SFG) for natural language generation. In particular, it describes the implementation of a fragment of the grammar of German in the computational framework of KOMET-PENMAN for multilingual generation. The text also presents a specification of explicit well-formedness constraints on syntagmatic structure which are defined in the form of typed feature structures. It thus achieves a model of systemic functional grammar that unites both the strengths of systemics, such as stratification, functional diversification
Vilic, Adnan; Petersen, John Asger; Hoppe, Karsten
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients’ physiological condition during an admission, which is assessed by electronically...... monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see...... drastic changes in the patients’ health, and thereby enabling staff to see where in the journal critical events have taken place....
Full Text Available The Quran is a scripture that acts as the main reference to people which their religion is Islam. It covers information from politics to science, with vast amount of information that requires effort to uncover the knowledge behind it. Today, the emergence of smartphones has led to the development of a wide-range application for enhancing knowledge-seeking activities. This project proposes a mobile application that is taking a natural language approach to searching topics in the Quran based on keyword searching. The benefit of the application is two-fold; it is intuitive and it saves time.
The aim of this bachelor thesis is to explore this image label database coming from the ESP game from the natural language processing (NLP) point of view. ESP game is an online game, in which human players do useful work - they label images. The output of the ESP game is then a database of images and their labels. What interests us is whether the data collected in the process of labeling images will be of any use in NLP tasks. Specifically, we are interested in the tasks of automatic corefere...
It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.
Full Text Available Abstract Background Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose. Methods We tried to detect adverse events from narrative text data of electronic medical records by using a natural language processing method. We made syntactic category decision rules to detect inpatient falls from text data in electronic medical records. We compared how often the true fall events were recorded in various sources of data including progress notes, discharge summaries, image order entries and incident reports. We applied the rules to these data sources and compared F-measures to detect falls between these data sources with reference to the results of a manual chart review. The lag time between event occurrence and data submission and the degree of injury were compared. Results We made 170 syntactic rules to detect inpatient falls by using a natural language processing method. Information on true fall events was most frequently recorded in progress notes (100%, incident reports (65.0% and image order entries (12.5%. However, F-measure to detect falls using the rules was poor when using progress notes (0.12 and discharge summaries (0.24 compared with that when using incident reports (1.00 and image order entries (0.91. Since the results suggested that incident reports and image order entries were possible data sources for prompt detection of serious falls, we focused on a comparison of falls found by incident reports and image order entries. Injury caused by falls found by image order entries was significantly more severe than falls detected by
This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both
Bruce Ian Carlin; Gustavo Manso
Investor sophistication has lagged behind the growing complexity of retail financial markets. To explore this, we develop a dynamic model to study the interaction between obfuscation and investor sophistication in mutual fund markets. Taking into account different learning mechanisms within the investor population, we characterize the optimal timing of obfuscation for financial institutions who offer retail products. We show that educational initiatives that are directed to facilitate learnin...
Full Text Available Musical skills and expertise vary greatly in Western societies. Individuals can differ in their repertoire of musical behaviours as well as in the level of skill they display for any single musical behaviour. The types of musical behaviours we refer to here are broad, ranging from performance on an instrument and listening expertise, to the ability to employ music in functional settings or to communicate about music. In this paper, we first describe the concept of 'musical sophistication' which can be used to describe the multi-faceted nature of musical expertise. Next, we develop a novel measurement instrument, the Goldsmiths Musical Sophistication Index (Gold-MSI to assess self-reported musical skills and behaviours on multiple dimensions in the general population using a large Internet sample (n = 147,636. Thirdly, we report results from several lab studies, demonstrating that the Gold-MSI possesses good psychometric properties, and that self-reported musical sophistication is associated with performance on two listening tasks. Finally, we identify occupation, occupational status, age, gender, and wealth as the main socio-demographic factors associated with musical sophistication. Results are discussed in terms of theoretical accounts of implicit and statistical music learning and with regard to social conditions of sophisticated musical engagement.
Müllensiefen, Daniel; Gingras, Bruno; Musil, Jason; Stewart, Lauren
Musical skills and expertise vary greatly in Western societies. Individuals can differ in their repertoire of musical behaviours as well as in the level of skill they display for any single musical behaviour. The types of musical behaviours we refer to here are broad, ranging from performance on an instrument and listening expertise, to the ability to employ music in functional settings or to communicate about music. In this paper, we first describe the concept of 'musical sophistication' which can be used to describe the multi-faceted nature of musical expertise. Next, we develop a novel measurement instrument, the Goldsmiths Musical Sophistication Index (Gold-MSI) to assess self-reported musical skills and behaviours on multiple dimensions in the general population using a large Internet sample (n = 147,636). Thirdly, we report results from several lab studies, demonstrating that the Gold-MSI possesses good psychometric properties, and that self-reported musical sophistication is associated with performance on two listening tasks. Finally, we identify occupation, occupational status, age, gender, and wealth as the main socio-demographic factors associated with musical sophistication. Results are discussed in terms of theoretical accounts of implicit and statistical music learning and with regard to social conditions of sophisticated musical engagement.
Full Text Available This paper investigates the interplay of constructed action and the clause in Finnish Sign Language (FinSL. Constructed action is a form of gestural enactment in which the signers use their hands, face and other parts of the body to represent the actions, thoughts or feelings of someone they are referring to in the discourse. With the help of frequencies calculated from corpus data, this article shows firstly that when FinSL signers are narrating a story, there are differences in how they use constructed action. Then the paper argues that there are differences also in the prototypical structure, linkage type and non-manual activity of clauses, depending on the presence or non-presence of constructed action. Finally, taking the view that gesturality is an integral part of language, the paper discusses the nature of syntax in sign languages and proposes a conceptualization in which syntax is seen as a set of norms distributed on a continuum between a categorial-conventional end and a gradient-unconventional end.
Rice, Mabel L
Future perspectives on children with language impairments are framed from what is known about children with specific language impairment (SLI). A summary of the current state of services is followed by discussion of how these children can be overlooked and misunderstood and consideration of why it is so hard for some children to acquire language when it is effortless for most children. Genetic influences are highlighted, with the suggestion that nature plus nurture should be considered in present as well as future intervention approaches. A nurture perspective highlights the family context of the likelihood of SLI for some of the children. Future models of the causal pathways may provide more specific information to guide gene-treatment decisions, in ways parallel to current personalized medicine approaches. Future treatment options can build on the potential of electronic technologies and social media to provide personalized treatment methods available at a time and place convenient for the person to use as often as desired. The speech-language pathologist could oversee a wide range of treatment options and monitor evidence provided electronically to evaluate progress and plan future treatment steps. Most importantly, future methods can provide lifelong language acquisition activities that maintain the privacy and dignity of persons with language impairment, and in so doing will in turn enhance the effectiveness of speech-language pathologists. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
Malamud, B. D.; Rhodes, F. H. T.
This paper explores natural hazards teaching and communications through the use of a literary anthology of writings about the earth aimed at non-experts. Teaching natural hazards in high-school and university introductory Earth Science and Geography courses revolves mostly around lectures, examinations, and laboratory demonstrations/activities. Often the results of such a course are that a student 'memorizes' the answers, and is penalized when they miss a given fact [e.g., "You lost one point because you were off by 50 km/hr on the wind speed of an F5 tornado."] Although facts and general methodologies are certainly important when teaching natural hazards, it is a strong motivation to a student's assimilation of, and enthusiasm for, this knowledge, if supplemented by writings about the Earth. In this paper, we discuss a literary anthology which we developed [Language of the Earth, Rhodes, Stone, Malamud, Wiley-Blackwell, 2008] which includes many descriptions about natural hazards. Using first- and second-hand accounts of landslides, earthquakes, tsunamis, floods and volcanic eruptions, through the writings of McPhee, Gaskill, Voltaire, Austin, Cloos, and many others, hazards become 'alive', and more than 'just' a compilation of facts and processes. Using short excerpts such as these, or other similar anthologies, of remarkably written accounts and discussions about natural hazards results in 'dry' facts becoming more than just facts. These often highly personal viewpoints of our catostrophic world, provide a useful supplement to a student's understanding of the turbulent world in which we live.
Pazos R, Rodolfo A; Aguirre L, Marco A; González B, Juan J; Martínez F, José A; Pérez O, Joaquín; Verástegui O, Andrés A
In the last decades the popularity of natural language interfaces to databases (NLIDBs) has increased, because in many cases information obtained from them is used for making important business decisions. Unfortunately, the complexity of their customization by database administrators make them difficult to use. In order for a NLIDB to obtain a high percentage of correctly translated queries, it is necessary that it is correctly customized for the database to be queried. In most cases the performance reported in NLIDB literature is the highest possible; i.e., the performance obtained when the interfaces were customized by the implementers. However, for end users it is more important the performance that the interface can yield when the NLIDB is customized by someone different from the implementers. Unfortunately, there exist very few articles that report NLIDB performance when the NLIDBs are not customized by the implementers. This article presents a semantically-enriched data dictionary (which permits solving many of the problems that occur when translating from natural language to SQL) and an experiment in which two groups of undergraduate students customized our NLIDB and English language frontend (ELF), considered one of the best available commercial NLIDBs. The experimental results show that, when customized by the first group, our NLIDB obtained a 44.69 % of correctly answered queries and ELF 11.83 % for the ATIS database, and when customized by the second group, our NLIDB attained 77.05 % and ELF 13.48 %. The performance attained by our NLIDB, when customized by ourselves was 90 %.
Amaechi Uneke Enyi
Full Text Available The study entitled. “Language and Interactional Discourse: Deconstructing the Talk - Generating Machinery in Natural Conversation,” is an analysis of spontaneous and informal conversation. The study, carried out in the theoretical and methodological tradition of Ethnomethodology, was aimed at explicating how ordinary talk is organized and produced, how people coordinate their talk –in- interaction, how meanings are determined, and the role of talk in the wider social processes. The study followed the basic assumption of conversation analysis which is, that talk is not just a product of two ‘speakers - hearers’ who attempt to exchange information or convey messages to each other. Rather, participants in conversation are seen to be mutually orienting to, and collaborating in order to achieve orderly and meaningful communication. The analytic objective is therefore to make clear these procedures on which speakers rely to produce utterances and by which they make sense of other speakers’ talk. The datum used for this study was a recorded informal conversation between two (and later three middle- class civil servants who are friends. The recording was done in such a way that the participants were not aware that they were being recorded. The recording was later transcribed in a way that we believe is faithful to the spontaneity and informality of the talk. Our finding showed that conversation has its own features and is an ordered and structured social day by- day event. Specifically, utterances are designed and informed by organized procedures, methods and resources which are tied to the contexts in which they are produced, and which participants are privy to by virtue of their membership of a culture or a natural language community. Keywords: Language, Discourse and Conversation
Lane, H. Chad; Vanlehn, Kurt
For beginning programmers, inadequate problem solving and planning skills are among the most salient of their weaknesses. In this paper, we test the efficacy of natural language tutoring to teach and scaffold acquisition of these skills. We describe ProPL (Pro-PELL), a dialogue-based intelligent tutoring system that elicits goal decompositions and program plans from students in natural language. The system uses a variety of tutoring tactics that leverage students' intuitive understandings of the problem, how it might be solved, and the underlying concepts of programming. We report the results of a small-scale evaluation comparing students who used ProPL with a control group who read the same content. Our primary findings are that students who received tutoring from ProPL seem to have developed an improved ability to solve the composition problem and displayed behaviors that suggest they were able to think at greater levels of abstraction than students in the read-only group.
Kreimeyer, Kory; Foster, Matthew; Pandey, Abhishek; Arya, Nina; Halford, Gwendolyn; Jones, Sandra F; Forshee, Richard; Walderhaug, Mark; Botsis, Taxiarchis
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP. Copyright © 2017 Elsevier Inc. All rights reserved.
Full Text Available Bahasa adalah sebuah cara berkomunikasi secara sistematis dengan menggunakan suara atau simbol-simbol yang memiliki arti, yang diucapkan melalui mulut. Bahasa juga ditulis dengan mengikuti kaidah yang berlaku. Salah satu bahasa yang banyak digunakan di belahan dunia adalah Bahasa Inggris. Namun ada beberapa kendala apabila kita belajar kepada seorang guru atau instruktur. Waktu yang diberikan seorang guru, terbatas pada jam sekolah atau les saja. Bila siswa pulang sekolah atau les, maka yang bersangkutan harus belajar bahasa Inggris secara mandiri. Dari permasalahan di atas, muncul sebuah ide tentang bagaimana membuat sebuah penelitian yang berkaitan dengan pembuatan aplikasi yang mampu memberikan pengetahuan kepada siswa tentang bagaimana belajar bahasa Inggris secara mandiri baik dari perubahan kalimat postif menjadi kalimat negatif dan kalimat tanya. Disamping itu, aplikasi ini juga mampu memberikan pengetahuan tentang bagaimana mengucapkan kalimat dalam bahasa Inggris. Pada intinya kontribusi yang dapat diperoleh dari hasil penelitian ini adalah pihak terkait dari tingkat SMP sampai dengan SMU/SMK, dapat menggunakan aplikasi text to speech berbasis natural language processing untuk mempelajari tenses pada bahasa Inggris. Aplikasi ini dapat memperdengarkan kalimat-kalimat pada bahasa inggris dan dapat menyusun kalimat tanya dan kalimat negatif berdasarkan kalimat positifnya dalam beberapa tenses bahasa Inggris. Kata Kunci : Natural language processing, Text to speech
Alexandr I Krupnov
Full Text Available The article discusses the results of empirical study of the association between variables of persistence and academic achievement in foreign languages. The sample includes students of the Faculty of Physics, Mathematics and Natural Science at the RUDN University ( n = 115, divided into 5 subsamples, two of which are featured in the present study (the most and the least successful students subsamples. Persistence as a personality trait is studied within A.I. Krupnov’s system-functional approach. A.I. Krupnov’s paper-and-pencil test was used to measure persistence variables. Academic achievement was measured according to the four parameters: Phonetics, Grammar, Speaking and Political vocabulary based on the grades students received during the academic year. The analysis revealed that persistence displays different associations with academic achievement variables in more and less successful students subsamples, the general prominence of this trait is more important for unsuccessful students. Phonetics is the academic achievement variable most associated with persistence due to its nature, a skill one can acquire through hard work and practice which is the definition of persistence. Grammar as an academic achievement variable is not associated with persistence and probably relates to other factors. Unsuccessful students may have difficulties in separating various aspects of language acquisition from each other which should be taken into consideration by the teachers.
Hunter, James; Freer, Yvonne; Gatt, Albert; Reiter, Ehud; Sripada, Somayajulu; Sykes, Cindy
Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software. Copyright © 2012 Elsevier B.V. All rights reserved.
Genuardi, Michael T.
One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.
Paredes-Valverde, Mario Andrés
The semantic Web aims to provide to Web information with a well-defined meaning and make it understandable not only by humans but also by computers, thus allowing the automation, integration and reuse of high-quality information across different applications. However, current information retrieval mechanisms for semantic knowledge bases are intended to be only used by expert users. In this work, we propose a natural language interface that allows non-expert users the access to this kind of information through formulating queries in natural language. The present approach uses a domain-independent ontology model to represent the question\\'s structure and context. Also, this model allows determination of the answer type expected by the user based on a proposed question classification. To prove the effectiveness of our approach, we have conducted an evaluation in the music domain using LinkedBrainz, an effort to provide the MusicBrainz information as structured data on the Web by means of Semantic Web technologies. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.74 to 0.82 for a corpus of questions generated by a group of real-world end users. © The Author(s) 2015.
Paredes-Valverde, Mario André s; Valencia-Garcí a, Rafael; Rodriguez-Garcia, Miguel Angel; Colomo-Palacios, Ricardo; Alor-Herná ndez, Giner
The semantic Web aims to provide to Web information with a well-defined meaning and make it understandable not only by humans but also by computers, thus allowing the automation, integration and reuse of high-quality information across different applications. However, current information retrieval mechanisms for semantic knowledge bases are intended to be only used by expert users. In this work, we propose a natural language interface that allows non-expert users the access to this kind of information through formulating queries in natural language. The present approach uses a domain-independent ontology model to represent the question's structure and context. Also, this model allows determination of the answer type expected by the user based on a proposed question classification. To prove the effectiveness of our approach, we have conducted an evaluation in the music domain using LinkedBrainz, an effort to provide the MusicBrainz information as structured data on the Web by means of Semantic Web technologies. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.74 to 0.82 for a corpus of questions generated by a group of real-world end users. © The Author(s) 2015.
Full Text Available The seventh issue of Complex Systems Informatics and Modeling Quarterly presents five papers devoted to two distinct research topics: systems modeling and natural language processing (NLP. Both of these subjects are very important in computer science. Through modeling we can simplify the studied problem by concentrating on only one aspect at a time. Moreover, a properly constructed model allows the modeler to work on higher levels of abstraction and not having to concentrate on details. Since the size and complexity of information systems grows rapidly, creating good models of such systems is crucial. The analysis of natural language is slowly becoming a widely used tool in commerce and day to day life. Opinion mining allows recommender systems to provide accurate recommendations based on user-generated reviews. Speech recognition and NLP are the basis for such widely used personal assistants as Apple’s Siri, Microsoft’s Cortana, and Google Now. While a lot of work has already been done on natural language processing, the research usually concerns widely used languages, such as English. Consequently, natural language processing in languages other than English is very relevant subject and is addressed in this issue.
Ma, Cuixia; Dai, Guozhong
Natural User Interface is one of the important next generation interactions. Computers are not just the tools of many special people or areas but for most people. Ubiquitous computing makes the world magic and more comfortable. In the design domain, current systems, which need the detail information, cannot conveniently support the conceptual design of the early phrase. Pen and paper are the natural and simple tools to use in our daily life, especially in design domain. Gestures are the useful and natural mode in the interaction of pen-based. In natural UI, gestures can be introduced and used through the similar mode to the existing resources in interaction. But the gestures always are defined beforehand without the users' intention and recognized to represent something in certain applications without being transplanted to others. We provide the gesture description language (GDL) to try to cite the useful gestures to the applications conveniently. It can be used in terms of the independent control resource such as menus or icons in applications. So we give the idea from two perspectives: one from the application-dependent point of view and the other from the application-independent point of view.
Zech, John; Pain, Margaret; Titano, Joseph; Badgeley, Marcus; Schefflein, Javin; Su, Andres; Costa, Anthony; Bederson, Joshua; Lehar, Joseph; Oermann, Eric Karl
Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora. Head CT reports were preprocessed, and machine-analyzable features were constructed by using bag-of-words (BOW), word embedding, and Latent Dirichlet allocation-based approaches. Ultimately, 1004 head CT reports were manually labeled for findings of interest by physicians, and a subset of these were deemed critical findings. Lasso logistic regression was used to train models for physician-assigned labels on 602 of 1004 head CT reports (60%) using the constructed features, and the performance of these models was validated on a held-out 402 of 1004 reports (40%). Models were scored by area under the receiver operating characteristic curve (AUC), and aggregate AUC statistics were reported for (a) all labels, (b) critical labels, and (c) the presence of any critical finding in a report. Sensitivity, specificity, accuracy, and F1 score were reported for the best performing model's (a) predictions of all labels and (b) identification of reports containing critical findings. Results The best-performing model (BOW with unigrams, bigrams, and trigrams plus average word embeddings vector) had a held-out AUC of 0.966 for identifying the presence of any critical head CT finding and an average 0.957 AUC across all head CT findings. Sensitivity and specificity for identifying the presence of any critical finding were 92.59% (175 of 189) and 89.67% (191 of 213), respectively. Average sensitivity and specificity across all findings were 90.25% (1898 of 2103) and 91.72% (18 351 of 20 007), respectively. Simpler BOW methods achieved results competitive with those of more sophisticated approaches, with an average AUC for presence of any
Lusardi, Annamaria; Mitchell, Olivia S.; Curto, Vilsa
Using a special-purpose module implemented in the Health and Retirement Study, we evaluate financial sophistication in the American population over the age of 50. We combine several financial literacy questions into an overall index to highlight which questions best capture financial sophistication and examine the sensitivity of financial literacy responses to framing effects. Results show that many older respondents are not financially sophisticated: they fail to grasp essential aspects of risk diversification, asset valuation, portfolio choice, and investment fees. Subgroups with notable deficits include women, the least educated, non-Whites, and those over age 75. In view of the fact that retirees increasingly must take on responsibility for their own retirement security, such meager levels of knowledge have potentially serious and negative implications. PMID:28553191
Bodie, Graham D; Collins, William B; Jensen, Jakob D; Davis, Lashara A; Guntzviller, Lisa M; King, Andy J
This article develops a conceptualization and measure of cognitive health sophistication--the complexity of an individual's conceptual knowledge about health. Study 1 provides initial validity evidence for the measure--the Healthy-Unhealthy Other Instrument--by showing its association with other cognitive health constructs indicative of higher health sophistication. Study 2 presents data from a sample of low-income adults to provide evidence that the measure does not depend heavily on health-related vocabulary or ethnicity. Results from both studies suggest that the Healthy-Unhealthy Other Instrument can be used to capture variability in the sophistication or complexity of an individual's health-related schematic structures on the basis of responses to two simple open-ended questions. Methodological advantages of the Healthy-Unhealthy Other Instrument and suggestions for future research are highlighted in the discussion.
Lusardi, Annamaria; Mitchell, Olivia S; Curto, Vilsa
Using a special-purpose module implemented in the Health and Retirement Study, we evaluate financial sophistication in the American population over the age of 50. We combine several financial literacy questions into an overall index to highlight which questions best capture financial sophistication and examine the sensitivity of financial literacy responses to framing effects. Results show that many older respondents are not financially sophisticated: they fail to grasp essential aspects of risk diversification, asset valuation, portfolio choice, and investment fees. Subgroups with notable deficits include women, the least educated, non-Whites, and those over age 75. In view of the fact that retirees increasingly must take on responsibility for their own retirement security, such meager levels of knowledge have potentially serious and negative implications.
Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D
To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient
Lassiter, Daniel; Goodman, Noah D
The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments. Copyright © 2014 Elsevier B.V. All rights reserved.
Soysal, Ergin; Wang, Jingqi; Jiang, Min; Wu, Yonghui; Pakhomov, Serguei; Liu, Hongfang; Xu, Hua
Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: email@example.com.
Levitt, Ash; Schlauch, Robert C; Bartholow, Bruce D; Sher, Kenneth J
Examining the natural language college students use to describe various levels of intoxication can provide important insight into subjective perceptions of college alcohol use. Previous research (Levitt et al., Alcohol Clin Exp Res 2009; 33: 448) has shown that intoxication terms reflect moderate and heavy levels of intoxication and that self-use of these terms differs by gender among college students. However, it is still unknown whether these terms similarly apply to other individuals and, if so, whether similar gender differences exist. To address these issues, the current study examined the application of intoxication terms to characters in experimentally manipulated vignettes of naturalistic drinking situations within a sample of university undergraduates (n = 145). Findings supported and extended previous research by showing that other-directed applications of intoxication terms are similar to self-directed applications and depend on the gender of both the target and the user. Specifically, moderate intoxication terms were applied to and from women more than men, even when the character was heavily intoxicated, whereas heavy intoxication terms were applied to and from men more than women. The findings suggest that gender differences in the application of intoxication terms are other-directed as well as self-directed and that intoxication language can inform gender-specific prevention and intervention efforts targeting problematic alcohol use among college students. Copyright © 2013 by the Research Society on Alcoholism.
Payne, Philip R O; Kwok, Alan; Dhaval, Rakesh; Borlawsky, Tara B
The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.
Juan Andres Laura
Full Text Available In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.
In this paper, I investigate a problem of finding most similar music tracks using, popular in Natural Language Processing, techniques like: TF-IDF and LDA. I de ned document as music track. Each music track is transformed to spectrogram, thanks that, I can use well known techniques to get words from images. I used SURF operation to detect characteristic points and novel approach for their description. The standard kmeans was used for clusterization. Clusterization is here identical with dictionary making, so after that I can transform spectrograms to text documents and perform TF-IDF and LDA. At the final, I can make a query in an obtained vector space. The research was done on 16 music tracks for training and 336 for testing, that are splitted in four categories: Hiphop, Jazz, Metal and Pop. Although used technique is completely unsupervised, results are satisfactory and encouraging to further research.
Full Text Available In the last decades, Natural Language Processing (NLP has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure: first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.
Bosco, Cristina; Delmonte, Rodolfo; Moschitti, Alessandro; Simi, Maria
The papers collected in this volume are selected as a sample of the progress in Natural Language Processing (NLP) performed within the Italian NLP community and especially attested by the PARLI project. PARLI (Portale per l’Accesso alle Risorse in Lingua Italiana) is a project partially funded by the Ministero Italiano per l’Università e la Ricerca (PRIN 2008) from 2008 to 2012 for monitoring and fostering the harmonic growth and coordination of the activities of Italian NLP. It was proposed by various teams of researchers working in Italian universities and research institutions. According to the spirit of the PARLI project, most of the resources and tools created within the project and here described are freely distributed and they did not terminate their life at the end of the project itself, hoping they could be a key factor in future development of computational linguistics.
Pai, Vinay M; Rodgers, Mary; Conroy, Richard; Luo, James; Zhou, Ruixia; Seto, Belinda
In April 2012, the National Institutes of Health organized a two-day workshop entitled 'Natural Language Processing: State of the Art, Future Directions and Applications for Enhancing Clinical Decision-Making' (NLP-CDS). This report is a summary of the discussions during the second day of the workshop. Collectively, the workshop presenters and participants emphasized the need for unstructured clinical notes to be included in the decision making workflow and the need for individualized longitudinal data tracking. The workshop also discussed the need to: (1) combine evidence-based literature and patient records with machine-learning and prediction models; (2) provide trusted and reproducible clinical advice; (3) prioritize evidence and test results; and (4) engage healthcare professionals, caregivers, and patients. The overall consensus of the NLP-CDS workshop was that there are promising opportunities for NLP and CDS to deliver cognitive support for healthcare professionals, caregivers, and patients.
Redman, Joseph S; Natarajan, Yamini; Hou, Jason K; Wang, Jingqi; Hanif, Muzammil; Feng, Hua; Kramer, Jennifer R; Desiderio, Roxanne; Xu, Hua; El-Serag, Hashem B; Kanwal, Fasiha
Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records. We utilized this technique to develop algorithms capable of "reading" full-text radiology reports to accurately identify the presence of fatty liver disease. Abdominal ultrasound, computerized tomography, and magnetic resonance imaging reports were retrieved from the Veterans Affairs Corporate Data Warehouse from a random national sample of 652 patients. Radiographic fatty liver disease was determined by manual review by two physicians and verified with an expert radiologist. A split validation method was utilized for algorithm development. For all three imaging modalities, the algorithms could identify fatty liver disease with >90% recall and precision, with F-measures >90%. These algorithms could be used to rapidly screen patient records to establish a large cohort to facilitate epidemiological and clinical studies and examine the clinic course and outcomes of patients with radiographic hepatic steatosis.
Li, Muqun; Carrell, David; Aberdeen, John; Hirschman, Lynette; Kirby, Jacqueline; Li, Bo; Vorobeychik, Yevgeniy; Malin, Bradley A
Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient. We introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator). Our results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the
Goldstein, Ayelet; Shahar, Yuval
Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains - Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years.
El Saadawi, Gilan M.; Tseytlin, Eugene; Legowski, Elizabeth; Jukic, Drazen; Castine, Melissa; Fine, Jeffrey; Gormley, Robert; Crowley, Rebecca S.
Introduction We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback. Research Questions We evaluated (1) the performance of our natural language system, (2) the effect of the system on learning (3) the effect of feedback timing on learning gains and (4) the effect of ReportTutor on performance to self-assessment correlations. Methods The study uses a crossover 2×2 factorial design. We recruited 20 subjects from 4 academic programs. Subjects were randomly assigned to one of the four conditions - two conditions for the immediate interface, and two for the delayed interface. An expert dermatopathologist created a reference standard and 2 board certified AP/CP pathology fellows manually coded the residents' assessment reports. Subjects were given the opportunity to self grade their performance and we used a survey to determine student response to both interfaces. Results Our results show a highly significant improvement in report writing after one tutoring session with 4-fold increase in the learning gains with both interfaces but no effect of feedback timing on performance gains. Residents who used the immediate feedback interface first experienced a feature learning gain that is correlated with the number of cases they viewed. There was no correlation between performance and self-assessment in either condition. PMID:17934789
The complex environment of the typical research laboratory requires flexible process control. This program provides natural language process control from an IBM PC or compatible machine. Sometimes process control schedules require changes frequently, even several times per day. These changes may include adding, deleting, and rearranging steps in a process. This program sets up a process control system that can either run without an operator, or be run by workers with limited programming skills. The software system includes three programs. Two of the programs, written in FORTRAN77, record data and control research processes. The third program, written in Pascal, generates the FORTRAN subroutines used by the other two programs to identify the user commands with the user-written device drivers. The software system also includes an input data set which allows the user to define the user commands which are to be executed by the computer. To set the system up the operator writes device driver routines for all of the controlled devices. Once set up, this system requires only an input file containing natural language command lines which tell the system what to do and when to do it. The operator can make up custom commands for operating and taking data from external research equipment at any time of the day or night without the operator in attendance. This process control system requires a personal computer operating under MS-DOS with suitable hardware interfaces to all controlled devices. The program requires a FORTRAN77 compiler and user-written device drivers. This program was developed in 1989 and has a memory requirement of about 62 Kbytes.
Baneyx, Audrey; Charlet, Jean; Jaulent, Marie-Christine
Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.
Fund raising, it is argued, needs sophisticated prospect research. Professional prospect researchers play an important role in helping to identify prospective donors and also in helping to stimulate interest in gift giving. A sample of an individual work-up on a donor and a bibliography are provided. (MLW)
Juan Pablo Sánchez Hernández
Full Text Available Procles, cited by Pausanias (in the imperfect tense about a display in Rome and for an opinion about Pyrrhus of Epirus, probably was not a historian of Hellenistic date, but a contemporary sophist whom Pausanias encountered in person in Rome.
Kesting, Peter; Günzel-Jensen, Franziska
, and Spreadshirt, this article develops a framework that introduces five business model sophistication strategies: (1) uncover additional functions of your product, (2) identify strategic benefits for third parties, (3) take advantage of economies of scope, (4) utilize cross-selling opportunities, and (5) involve...
Swartz, Jordan; Koziatek, Christian; Theobald, Jason; Smith, Silas; Iturrate, Eduardo
Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (percentage of tests positive for the diagnostic entity of interest). However, determining diagnostic yield typically requires either time-consuming, manual review of radiology reports or the use of complex and/or proprietary natural language processing software. The objectives of this study were twofold: 1) to develop and implement a simple, user-configurable, and open-source natural language processing tool to classify radiology reports with high accuracy and 2) to use the results of the tool to design a provider-specific VTE imaging dashboard, consisting of both utilization rate and diagnostic yield. Two physicians reviewed a training set of 400 lower extremity ultrasound (UTZ) and computed tomography pulmonary angiogram (CTPA) reports to understand the language used in VTE-positive and VTE-negative reports. The insights from this review informed the arguments to the five modifiable parameters of the NLP tool. A validation set of 2,000 studies was then independently classified by the reviewers and by the tool; the classifications were compared and the performance of the tool was calculated. The tool was highly accurate in classifying the presence and absence of VTE for both the UTZ (sensitivity 95.7%; 95% CI 91.5-99.8, specificity 100%; 95% CI 100-100) and CTPA reports (sensitivity 97.1%; 95% CI 94.3-99.9, specificity 98.6%; 95% CI 97.8-99.4). The diagnostic yield was then calculated at the individual provider level and the imaging dashboard was created. We have created a novel NLP tool designed for users without a background in computer programming, which has been used to classify venous thromboembolism reports with a high degree of accuracy. The tool is open-source and available for download at http
Full Text Available Abstract Background The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP pipeline for AEFI reports. Methods We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1 error detection, (2 word list generation, (3 word list disambiguation and (4 error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction. Results We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV for the spell checker were 74% (95% CI: 74–75, 100% (95% CI: 100–100, and 47% (95% CI: 46%–48%, respectively. Conclusion We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available
Wächter, Mirko; Ovchinnikova, Ekaterina; Wittenbeck, Valerij
We propose an approach for instructing a robot using natural language to solve complex tasks in a dynamic environment. In this study, we elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate....... The framework is implemented within the robot development environment ArmarX. We evaluate the framework on the humanoid robot ARMAR-III in the context of two experiments: a demonstration of the real execution of a complex task in the kitchen environment on ARMAR-III and an experiment with untrained users...
McColl, Derek; Jiang, Chuan; Nejat, Goldie
For social robots to be successfully integrated and accepted within society, they need to be able to interpret human social cues that are displayed through natural modes of communication. In particular, a key challenge in the design of social robots is developing the robot's ability to recognize a person's affective states (emotions, moods, and attitudes) in order to respond appropriately during social human-robot interactions (HRIs). In this paper, we present and discuss social HRI experiments we have conducted to investigate the development of an accessibility-aware social robot able to autonomously determine a person's degree of accessibility (rapport, openness) toward the robot based on the person's natural static body language. In particular, we present two one-on-one HRI experiments to: 1) determine the performance of our automated system in being able to recognize and classify a person's accessibility levels and 2) investigate how people interact with an accessibility-aware robot which determines its own behaviors based on a person's speech and accessibility levels.
Musolino, Julien; Landau, Barbara
In this article, we discuss two experiments of nature and their implications for the sciences of the mind. The first, Williams syndrome, bears on one of cognitive science's holy grails: the possibility of unravelling the causal chain between genes and cognition. We sketch the outline of a general framework to study the relationship between genes and cognition, focusing as our case study on the development of language in individuals with Williams syndrome. Our approach emphasizes the role of three key ingredients: the need to specify a clear level of analysis, the need to provide a theoretical account of the relevant cognitive structure at that level, and the importance of the (typical) developmental process itself. The promise offered by the case of Williams syndrome has also given rise to two strongly conflicting theoretical approaches-modularity and neuroconstructivism-themselves offshoots of a perennial debate between nativism and empiricism. We apply our framework to explore the tension created by these two conflicting perspectives. To this end, we discuss a second experiment of nature, which allows us to compare the two competing perspectives in what comes close to a controlled experimental setting. From this comparison, we conclude that the "meaningful debate assumption", a widespread assumption suggesting that neuroconstructivism and modularity address the same questions and represent genuine theoretical alternatives, rests on a fallacy.
Beyer, Sebastian E; McKee, Brady J; Regis, Shawn M; McKee, Andrea B; Flacke, Sebastian; El Saadawi, Gilan; Wald, Christoph
Our aim was to train a natural language processing (NLP) algorithm to capture imaging characteristics of lung nodules reported in a structured CT report and suggest the applicable Lung-RADS™ (LR) category. Our study included structured, clinical reports of consecutive CT lung screening (CTLS) exams performed from 08/2014 to 08/2015 at an ACR accredited Lung Cancer Screening Center. All patients screened were at high-risk for lung cancer according to the NCCN Guidelines ® . All exams were interpreted by one of three radiologists credentialed to read CTLS exams using LR using a standard reporting template. Training and test sets consisted of consecutive exams. Lung screening exams were divided into two groups: three training sets (500, 120, and 383 reports each) and one final evaluation set (498 reports). NLP algorithm results were compared with the gold standard of LR category assigned by the radiologist. The sensitivity/specificity of the NLP algorithm to correctly assign LR categories for suspicious nodules (LR 4) and positive nodules (LR 3/4) were 74.1%/98.6% and 75.0%/98.8% respectively. The majority of mismatches occurred in cases where pulmonary findings were present not currently addressed by LR. Misclassifications also resulted from the failure to identify exams as follow-up and the failure to completely characterize part-solid nodules. In a sub-group analysis among structured reports with standardized language, the sensitivity and specificity to detect LR 4 nodules were 87.0% and 99.5%, respectively. An NLP system can accurately suggest the appropriate LR category from CTLS exam findings when standardized reporting is used.
Rainer Andergassen; Guido Candela
This paper investigates the effectiveness of development strategies for tourism destinations. We argue that resource investments unambiguously increase tourism revenues and that increasing the degree of tourism sophistication, that is increasing the variety of tourism related goods and services, increases tourism activity and decreases the perceived quality of the destination's resource endowment, leading to an ambiguous effect on tourism revenues. We disentangle these two effects and charact...
Khanna, Anirudh; Das, Bhagwan; Pandey, Bishwajeet
With the advent of AI and IoT, the idea of incorporating smart things/appliances in our day to day life is converting into a reality. The paper discusses the possibilities and potential of designing IoT systems which can be controlled via natural language, with help of Quick Script as a development...
He, Qiwei; Veldkamp, Bernard P.; Glas, Cornelis A.W.; de Vries, Theo
Patients’ narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four
Dessus, Philippe; Trausan-Matu, Stefan; Van Rosmalen, Peter; Wild, Fridolin
Dessus, P., Trausan-Matu, S., Van Rosmalen, P., & Wild, F. (Eds.) (2009). AIED 2009 Workshops Proceedings Volume 10 Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity. In S. D. Craig & D. Dicheva (Eds.), AIED 2009: 14th International Conference in Artificial
Sermet, M. Y.; Demir, I.; Krajewski, W. F.
The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to flood inundation maps, real-time flood conditions, flood forecasts, flood-related data, information and interactive visualizations for communities in Iowa. The IFIS is designed for use by general public, often people with no domain knowledge and limited general science background. To improve effective communication with such audience, we have introduced a voice-enabled knowledge engine on flood related issues in IFIS. Instead of navigating within many features and interfaces of the information system and web-based sources, the system provides dynamic computations based on a collection of built-in data, analysis, and methods. The IFIS Knowledge Engine connects to real-time stream gauges, in-house data sources, analysis and visualization tools to answer natural language questions. Our goal is the systematization of data and modeling results on flood related issues in Iowa, and to provide an interface for definitive answers to factual queries. The goal of the knowledge engine is to make all flood related knowledge in Iowa easily accessible to everyone, and support voice-enabled natural language input. We aim to integrate and curate all flood related data, implement analytical and visualization tools, and make it possible to compute answers from questions. The IFIS explicitly implements analytical methods and models, as algorithms, and curates all flood related data and resources so that all these resources are computable. The IFIS Knowledge Engine computes the answer by deriving it from its computational knowledge base. The knowledge engine processes the statement, access data warehouse, run complex database queries on the server-side and return outputs in various formats. This presentation provides an overview of IFIS Knowledge Engine, its unique information interface and functionality as an educational tool, and discusses the future plans
Byers-Heinlein, Krista; Chen, Ke Heng; Xu, Fei
Languages function as independent and distinct conventional systems, and so each language uses different words to label the same objects. This study investigated whether 2-year-old children recognize that speakers of their native language and speakers of a foreign language do not share the same knowledge. Two groups of children unfamiliar with Mandarin were tested: monolingual English-learning children (n=24) and bilingual children learning English and another language (n=24). An English speaker taught children the novel label fep. On English mutual exclusivity trials, the speaker asked for the referent of a novel label (wug) in the presence of the fep and a novel object. Both monolingual and bilingual children disambiguated the reference of the novel word using a mutual exclusivity strategy, choosing the novel object rather than the fep. On similar trials with a Mandarin speaker, children were asked to find the referent of a novel Mandarin label kuò. Monolinguals again chose the novel object rather than the object with the English label fep, even though the Mandarin speaker had no access to conventional English words. Bilinguals did not respond systematically to the Mandarin speaker, suggesting that they had enhanced understanding of the Mandarin speaker's ignorance of English words. The results indicate that monolingual children initially expect words to be conventionally shared across all speakers-native and foreign. Early bilingual experience facilitates children's discovery of the nature of foreign language words. Copyright © 2013 Elsevier Inc. All rights reserved.
Falomir, Zoe; Kluth, Thomas
The challenge of describing 3D real scenes is tackled in this paper using qualitative spatial descriptors. A key point to study is which qualitative descriptors to use and how these qualitative descriptors must be organized to produce a suitable cognitive explanation. In order to find answers, a survey test was carried out with human participants which openly described a scene containing some pieces of furniture. The data obtained in this survey are analysed, and taking this into account, the QSn3D computational approach was developed which uses a XBox 360 Kinect to obtain 3D data from a real indoor scene. Object features are computed on these 3D data to identify objects in indoor scenes. The object orientation is computed, and qualitative spatial relations between the objects are extracted. These qualitative spatial relations are the input to a grammar which applies saliency rules obtained from the survey study and generates cognitive natural language descriptions of scenes. Moreover, these qualitative descriptors can be expressed as first-order logical facts in Prolog for further reasoning. Finally, a validation study is carried out to test whether the descriptions provided by QSn3D approach are human readable. The obtained results show that their acceptability is higher than 82%.
Hassanpour, Saeed; Bay, Graham; Langlotz, Curtis P
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.
Full Text Available Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average. Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.
Full Text Available As we discuss, a stationary stochastic process is nonergodic when a random persistent topic can be detected in the infinite random text sampled from the process, whereas we call the process strongly nonergodic when an infinite sequence of independent random bits, called probabilistic facts, is needed to describe this topic completely. Replacing probabilistic facts with an algorithmically random sequence of bits, called algorithmic facts, we adapt this property back to ergodic processes. Subsequently, we call a process perigraphic if the number of algorithmic facts which can be inferred from a finite text sampled from the process grows like a power of the text length. We present a simple example of such a process. Moreover, we demonstrate an assertion which we call the theorem about facts and words. This proposition states that the number of probabilistic or algorithmic facts which can be inferred from a text drawn from a process must be roughly smaller than the number of distinct word-like strings detected in this text by means of the Prediction by Partial Matching (PPM compression algorithm. We also observe that the number of the word-like strings for a sample of plays by Shakespeare follows an empirical stepwise power law, in a stark contrast to Markov processes. Hence, we suppose that natural language considered as a process is not only non-Markov but also perigraphic.
Badr, Hoda; Milbury, Kathrin; Majeed, Nadia; Carmack, Cindy L.; Ahmad, Zeba; Gritz, Ellen R.
Objective This multimethod prospective study examined whether emotional disclosure and coping focus as conveyed through natural language use is associated with the psychological and marital adjustment of head and neck cancer patients and their spouses. Methods One-hundred twenty-three patients (85% men; age X‒=56.8 years, SD=10.4) and their spouses completed surveys prior to, following, and 4-months after engaging in a videotaped discussion about cancer in the laboratory. Linguistic Inquiry and Word Count (LIWC) software assessed counts of positive/negative emotion words and first-person singular (I-talk), second person (you-talk), and first-person plural (we-talk) pronouns. Using a Grounded Theory approach, discussions were also analyzed to describe how emotion words and pronouns were used and what was being discussed. Results Emotion words were most often used to disclose thoughts/feelings or worry/uncertainty about the future, and to express gratitude or acknowledgment to one’s partner. Although patients who disclosed more negative emotion during the discussion reported more positive mood following the discussion (ppsychological and marital adjustment were found. Patients used significantly more I-talk than spouses and spouses used significantly more you-talk than patients (p’sdistress at the 4-month follow-up assessment when their partners used more we-talk (p disclosure may be less important to one’s cancer adjustment than having a partner who one sees as instrumental to the coping process. PMID:27441867
Full Text Available Controlling robots by natural language (NL is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications.
Sevenster, Merlijn; Bozeman, Jeffrey; Cowhy, Andrea; Trost, William
To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We study through inter-annotator agreement and natural language processing (NLP) algorithm development the task of pairing measurements that quantify the same finding across consecutive radiology reports, such that each measurement is paired with at most one other ("partial uniqueness"). Ground truth is created based on 283 abdomen and 311 chest CT reports of 50 patients each. A pre-processing engine segments reports and extracts measurements. Thirteen features are developed based on volumetric similarity between measurements, semantic similarity between their respective narrative contexts and structural properties of their report positions. A Random Forest classifier (RF) integrates all features. A "mutual best match" (MBM) post-processor ensures partial uniqueness. In an end-to-end evaluation, RF has precision 0.841, recall 0.807, F-measure 0.824 and AUC 0.971; with MBM, which performs above chance level (P0.960) indicates that the task is well defined. Domain properties and inter-section differences are discussed to explain superior performance in abdomen. Enforcing partial uniqueness has mixed but minor effects on performance. A combined machine learning-filtering approach is proposed for pairing measurements, which can support prospective (supporting treatment response assessment) and retrospective purposes (data mining). Copyright © 2014 Elsevier Inc. All rights reserved.
Kerlyl, Alice; Hall, Phil; Bull, Susan
There is an extensive body of work on Intelligent Tutoring Systems: computer environments for education, teaching and training that adapt to the needs of the individual learner. Work on personalisation and adaptivity has included research into allowing the student user to enhance the system's adaptivity by improving the accuracy of the underlying learner model. Open Learner Modelling, where the system's model of the user's knowledge is revealed to the user, has been proposed to support student reflection on their learning. Increased accuracy of the learner model can be obtained by the student and system jointly negotiating the learner model. We present the initial investigations into a system to allow people to negotiate the model of their understanding of a topic in natural language. This paper discusses the development and capabilities of both conversational agents (or chatbots) and Intelligent Tutoring Systems, in particular Open Learner Modelling. We describe a Wizard-of-Oz experiment to investigate the feasibility of using a chatbot to support negotiation, and conclude that a fusion of the two fields can lead to developing negotiation techniques for chatbots and the enhancement of the Open Learner Model. This technology, if successful, could have widespread application in schools, universities and other training scenarios.
A. E. Pismak
Full Text Available Subject of Research. The paper is focused on Wiktionary articles structural organization in the aspect of its usage as the base for semantic network. Wiktionary community references, article templates and articles markup features are analyzed. The problem of numerical estimation for semantic similarity of structural elements in Wiktionary articles is considered. Analysis of existing software for semantic similarity estimation of such elements is carried out; algorithms of their functioning are studied; their advantages and disadvantages are shown. Methods. Mathematical statistics methods were used to analyze Wiktionary articles markup features. The method of semantic similarity computing based on statistics data for compared structural elements was proposed.Main Results. We have concluded that there is no possibility for direct use of Wiktionary articles as the source for semantic network. We have proposed to find hidden similarity between article elements, and for that purpose we have developed the algorithm for calculation of confidence coefficients proving that each pair of sentences is semantically near. The research of quantitative and qualitative characteristics for the developed algorithm has shown its major performance advantage over the other existing solutions in the presence of insignificantly higher error rate. Practical Relevance. The resulting algorithm may be useful in developing tools for automatic Wiktionary articles parsing. The developed method could be used in computing of semantic similarity for short text fragments in natural language in case of algorithm performance requirements are higher than its accuracy specifications.
Dang, Pragya A; Kalra, Mannudeep K; Blake, Michael A; Schultz, Thomas J; Stout, Markus; Lemay, Paul R; Freshman, David J; Halpern, Elkan F; Dreyer, Keith J
The study purpose was to describe the use of natural language processing (NLP) and online analytic processing (OLAP) for assessing patterns in recommendations in unstructured radiology reports on the basis of patient and imaging characteristics, such as age, gender, referring physicians, radiology subspecialty, modality, indications, diseases, and patient status (inpatient vs outpatient). A database of 4,279,179 radiology reports from a single tertiary health care center during a 10-year period (1995-2004) was created. The database includes reports of computed tomography, magnetic resonance imaging, fluoroscopy, nuclear medicine, ultrasound, radiography, mammography, angiography, special procedures, and unclassified imaging tests with patient demographics. A clinical data mining and analysis NLP program (Leximer, Nuance Inc, Burlington, Massachusetts) in conjunction with OLAP was used for classifying reports into those with recommendations (I(REC)) and without recommendations (N(REC)) for imaging and determining I(REC) rates for different patient age groups, gender, imaging modalities, indications, diseases, subspecialties, and referring physicians. In addition, temporal trends for I(REC) were also determined. There was a significant difference in the I(REC) rates in different age groups, varying between 4.8% (10-19 years) and 9.5% (>70 years) (P OLAP revealed considerable differences between recommendation trends for different imaging modalities and other patient and imaging characteristics.
Yu. S. Hetsevich
Full Text Available The article focuses on the problems existing in text-to-speech synthesis. Different morphological, lexical and syntactical elements were localized with the help of the Belarusian unit of NooJ program. Those types of errors, which occur in Belarusian texts, were analyzed and corrected. Language model and part of speech tagging model were built. The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. The dictionary was enriched with new words for the further using it in the systems of Belarusian speech synthesis.
Burk, Robin K.
Computational natural language understanding and generation have been a goal of artificial intelligence since McCarthy, Minsky, Rochester and Shannon first proposed to spend the summer of 1956 studying this and related problems. Although statistical approaches dominate current natural language applications, two current research trends bring…
Weng, Wei-Hung; Wagholikar, Kavishwar B; McCray, Alexa T; Szolovits, Peter; Chueh, Henry C
The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets. The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied. Our study shows that a supervised
Maynard, Andrew D.; Warheit, David B.; Philbert, Martin A.
It has long been recognized that the physical form of materials can mediate their toxicity—the health impacts of asbestiform materials, industrial aerosols, and ambient particulate matter are prime examples. Yet over the past 20 years, toxicology research has suggested complex and previously unrecognized associations between material physicochemistry at the nanoscale and biological interactions. With the rapid rise of the field of nanotechnology and the design and production of increasingly complex nanoscale materials, it has become ever more important to understand how the physical form and chemical composition of these materials interact synergistically to determine toxicity. As a result, a new field of research has emerged—nanotoxicology. Research within this field is highlighting the importance of material physicochemical properties in how dose is understood, how materials are characterized in a manner that enables quantitative data interpretation and comparison, and how materials move within, interact with, and are transformed by biological systems. Yet many of the substances that are the focus of current nanotoxicology studies are relatively simple materials that are at the vanguard of a new era of complex materials. Over the next 50 years, there will be a need to understand the toxicology of increasingly sophisticated materials that exhibit novel, dynamic and multifaceted functionality. If the toxicology community is to meet the challenge of ensuring the safe use of this new generation of substances, it will need to move beyond “nano” toxicology and toward a new toxicology of sophisticated materials. Here, we present a brief overview of the current state of the science on the toxicology of nanoscale materials and focus on three emerging toxicology-based challenges presented by sophisticated materials that will become increasingly important over the next 50 years: identifying relevant materials for study, physicochemical characterization, and
Sutter, Matthias; Czermak, Simon; Feri, Francesco
Many important decisions require strategic sophistication. We examine experimentally whether teams act more strategically than individuals. We let individuals and teams make choices in simple games, and also elicit first- and second-order beliefs. We find that teams play the Nash equilibrium strategy significantly more often, and their choices are more often a best response to stated first order beliefs. Distributional preferences make equilibrium play less likely. Using a mixture model, the estimated probability to play strategically is 62% for teams, but only 40% for individuals. A model of noisy introspection reveals that teams differ from individuals in higher order beliefs. PMID:24926100
Golo, V.L.; Perelomov, A.M.
Two classes of the two-dimensional Euclidean chiral field theoreties are singled out: 1) the field phi(x) takes the values in the compact Hermitiam symmetric space 2) the field phi(x) takes the values in an orbit of the adjoint representation of the comcompact Lie group. The theories have sophisticated topological and rich analytical structures. They are considered with the help of topological invariants (topological charges). Explicit formulae for the topological charges are indicated, and the lower bound extimate for the action is given
Full Text Available This article compares capital budgeting techniques employed in listed and unlisted companies in Brazil. We surveyed the Chief Financial Officers (CFOs of 398 listed companies and 300 large unlisted companies, and based on 91 respondents, the results suggest that the CFOs of listed companies tend to use less simplistic methods more often, for example: NPV and CAPM, and that CFOs of unlisted companies are less likely to estimate the cost of equity, despite being large companies. These findings indicate that stock exchange listing may require greater sophistication of the capital budgeting process.
analogy from Wittgenstein’s term "language game" ( Wittgenstein , 1958). However, Dialogue-games represent knowledge people have about language as used to...and memory of narrative discourse. CoRtiiiive PsycholoRy, 1977, 9, 77-110. Wittgenstein , L. Philosophical inve-ÜRalions (3rd ed.). New York
Giovana Fracari Hautrive
Full Text Available Taking the theme literacy of deaf children is currently directing the eye to the practice teaching course that demands beyond the school. Questions moving to daily practice, became a challenge, requiring an investigative attitude. The article aims to problematize the process of literacy of deaf children. Reflection proposal emerges from daily practice. This structure is from yarns that include theoretical studies of Vigotskii (1989, 1994, 1996, 1998; Stumpf (2005, Quadros (1997; Bolzan (1998, 2002; Skliar (1997a, 1997b, 1998 . From which, problematizes the processes involved in the construction of written language. It is as a result, the importance of the instrumentalization of sign language as first language in education of deaf and learning of sign language writing. Important aspects for the deaf student is observed in the condition to be literate in their mother tongue. It points out the need for a redirect in the literacy of deaf children, so that important aspects of language and its role in the structuring of thought and its communicative aspect, are respected and considered in this process. Thus, it emphasizes the learning of the writing of sign language as fundamental, it should occupy a central role in the proposed teaching the class, encouraging the contradictions that put the student in a situation of cognitive conflict, while respecting the diversity inherent to each humans. It is considered that the production of sign language writing is an appropriate tool for the deaf students record their visual language.
Lott, Jason P; Boudreau, Denise M; Barnhill, Ray L; Weinstock, Martin A; Knopp, Eleanor; Piepkorn, Michael W; Elder, David E; Knezevich, Steven R; Baer, Andrew; Tosteson, Anna N A; Elmore, Joann G
Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic understanding of skin biopsy outcomes, specifically those of melanocytic origin. To determine population-based frequencies and distribution of histologically confirmed melanocytic lesions. A natural language processing (NLP)-based analysis of EMR pathology reports of adult patients who underwent skin biopsies at a large integrated health care delivery system in the US Pacific Northwest from January 1, 2007, through December 31, 2012. Skin biopsy procedure. The primary outcome was histopathologic diagnosis, obtained using an NLP-based system to process EMR pathology reports. We determined the percentage of diagnoses classified as melanocytic vs nonmelanocytic lesions. Diagnoses classified as melanocytic were further subclassified using the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) reporting schema into the following categories: class I (nevi and other benign proliferations such as mildly dysplastic lesions typically requiring no further treatment), class II (moderately dysplastic and other low-risk lesions that may merit narrow reexcision with skin biopsies, performed on 47 529 patients, were examined. Nearly 1 in 4 skin biopsies were of melanocytic lesions (23%; n = 18 715), which were distributed according to MPATH-Dx categories as follows: class I, 83.1% (n = 15 558); class II, 8.3% (n = 1548); class III, 4.5% (n = 842); class IV, 2.2% (n = 405); and class V, 1.9% (n = 362). Approximately one-quarter of skin biopsies resulted in diagnoses of melanocytic proliferations. These data provide the first population-based estimates across the spectrum of melanocytic lesions ranging from benign through dysplastic to malignant. These results may serve as a foundation for future
Ben Abacha, Asma; Dos Reis, Julio Cesar; Mrabet, Yassine; Pruski, Cédric; Da Silveira, Marcos
The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging. We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions. This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9. The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.
Jay, Caroline; Harper, Simon; Dunlop, Ian; Smith, Sam; Sufi, Shoaib; Goble, Carole; Buchan, Iain
Data discovery, particularly the discovery of key variables and their inter-relationships, is key to secondary data analysis, and in-turn, the evolving field of data science. Interface designers have presumed that their users are domain experts, and so they have provided complex interfaces to support these "experts." Such interfaces hark back to a time when searches needed to be accurate first time as there was a high computational cost associated with each search. Our work is part of a governmental research initiative between the medical and social research funding bodies to improve the use of social data in medical research. The cross-disciplinary nature of data science can make no assumptions regarding the domain expertise of a particular scientist, whose interests may intersect multiple domains. Here we consider the common requirement for scientists to seek archived data for secondary analysis. This has more in common with search needs of the "Google generation" than with their single-domain, single-tool forebears. Our study compares a Google-like interface with traditional ways of searching for noncomplex health data in a data archive. Two user interfaces are evaluated for the same set of tasks in extracting data from surveys stored in the UK Data Archive (UKDA). One interface, Web search, is "Google-like," enabling users to browse, search for, and view metadata about study variables, whereas the other, traditional search, has standard multioption user interface. Using a comprehensive set of tasks with 20 volunteers, we found that the Web search interface met data discovery needs and expectations better than the traditional search. A task × interface repeated measures analysis showed a main effect indicating that answers found through the Web search interface were more likely to be correct (F1,19=37.3, Pnatural language search interfaces for variable search supporting in particular: query reformulation; data browsing; faceted search; surrogates; relevance
Tseytlin, Eugene; Mitchell, Kevin; Legowski, Elizabeth; Corrigan, Julia; Chavan, Girish; Jacobson, Rebecca S
Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.
Kim, Brian J; Merchant, Madhur; Zheng, Chengyi; Thomas, Anil A; Contreras, Richard; Jacobsen, Steven J; Chien, Gary W
Natural language processing (NLP) software programs have been widely developed to transform complex free text into simplified organized data. Potential applications in the field of medicine include automated report summaries, physician alerts, patient repositories, electronic medical record (EMR) billing, and quality metric reports. Despite these prospects and the recent widespread adoption of EMR, NLP has been relatively underutilized. The objective of this study was to evaluate the performance of an internally developed NLP program in extracting select pathologic findings from radical prostatectomy specimen reports in the EMR. An NLP program was generated by a software engineer to extract key variables from prostatectomy reports in the EMR within our healthcare system, which included the TNM stage, Gleason grade, presence of a tertiary Gleason pattern, histologic subtype, size of dominant tumor nodule, seminal vesicle invasion (SVI), perineural invasion (PNI), angiolymphatic invasion (ALI), extracapsular extension (ECE), and surgical margin status (SMS). The program was validated by comparing NLP results to a gold standard compiled by two blinded manual reviewers for 100 random pathology reports. NLP demonstrated 100% accuracy for identifying the Gleason grade, presence of a tertiary Gleason pattern, SVI, ALI, and ECE. It also demonstrated near-perfect accuracy for extracting histologic subtype (99.0%), PNI (98.9%), TNM stage (98.0%), SMS (97.0%), and dominant tumor size (95.7%). The overall accuracy of NLP was 98.7%. NLP generated a result in report. This novel program demonstrated high accuracy and efficiency identifying key pathologic details from the prostatectomy report within an EMR system. NLP has the potential to assist urologists by summarizing and highlighting relevant information from verbose pathology reports. It may also facilitate future urologic research through the rapid and automated creation of large databases.
Zheng, Chengyi; Rashid, Nazia; Wu, Yi-Lin; Koblick, River; Lin, Antony T; Levy, Gerald D; Cheetham, T Craig
Gout flares are not well documented by diagnosis codes, making it difficult to conduct accurate database studies. We implemented a computer-based method to automatically identify gout flares using natural language processing (NLP) and machine learning (ML) from electronic clinical notes. Of 16,519 patients, 1,264 and 1,192 clinical notes from 2 separate sets of 100 patients were selected as the training and evaluation data sets, respectively, which were reviewed by rheumatologists. We created separate NLP searches to capture different aspects of gout flares. For each note, the NLP search outputs became the ML system inputs, which provided the final classification decisions. The note-level classifications were grouped into patient-level gout flares. Our NLP+ML results were validated using a gold standard data set and compared with the claims-based method used by prior literatures. For 16,519 patients with a diagnosis of gout and a prescription for a urate-lowering therapy, we identified 18,869 clinical notes as gout flare positive (sensitivity 82.1%, specificity 91.5%): 1,402 patients with ≥3 flares (sensitivity 93.5%, specificity 84.6%), 5,954 with 1 or 2 flares, and 9,163 with no flare (sensitivity 98.5%, specificity 96.4%). Our method identified more flare cases (18,869 versus 7,861) and patients with ≥3 flares (1,402 versus 516) when compared to the claims-based method. We developed a computer-based method (NLP and ML) to identify gout flares from the clinical notes. Our method was validated as an accurate tool for identifying gout flares with higher sensitivity and specificity compared to previous studies. Copyright © 2014 by the American College of Rheumatology.
Arika E Wieneke
Full Text Available Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%, and evaluation (324, 10% purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related. Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity, but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.
The following sections are included: * Definition of Dynamical Languages * Distinct Excluded Blocks * Definition and Properties * L and L″ in Chomsky Hierarchy * A Natural Equivalence Relation * Symbolic Flows * Symbolic Flows and Dynamical Languages * Subshifts of Finite Type * Sofic Systems * Graphs and Dynamical Languages * Graphs and Shannon-Graphs * Transitive Languages * Topological Entropy
Full Text Available Scientific publications written in natural language still play a central role as our knowledge source. However, due to the flood of publications, the literature survey process has become a highly time-consuming and tangled process, especially for novices of the discipline. Therefore, tools supporting the literature-survey process may help the individual scientist to explore new useful domains. Natural language processing (NLP is expected as one of the promising techniques to retrieve, abstract, and extract knowledge. In this contribution, NLP is firstly applied to the literature of chemical vapor deposition (CVD, which is a sub-discipline of materials science and is a complex and interdisciplinary field of research involving chemists, physicists, engineers, and materials scientists. Causal knowledge extraction from the literature is demonstrated using NLP.
Merker, Bjorn; Okanoya, Kazuo
Human languages are quintessentially historical phenomena. Every known aspect of linguistic form and content is subject to change in historical time (Lehmann, 1995; Bybee, 2004). Many facts of language, syntactic no less than semantic, find their explanation in the historical processes that generated them. If adpositions were once verbs, then the fact that they tend to occur on the same side of their arguments as do verbs ("cross-category harmony": Hawkins, 1983) is a matter of historical contingency rather than a reflection of inherent structural constraints on human language (Delancey, 1993).
Saunders, Daniel R; Bex, Peter J; Woods, Russell L
Crowdsourcing has become a valuable method for collecting medical research data. This approach, recruiting through open calls on the Web, is particularly useful for assembling large normative datasets. However, it is not known how natural language datasets collected over the Web differ from those collected under controlled laboratory conditions. To compare the natural language responses obtained from a crowdsourced sample of participants with responses collected in a conventional laboratory setting from participants recruited according to specific age and gender criteria. We collected natural language descriptions of 200 half-minute movie clips, from Amazon Mechanical Turk workers (crowdsourced) and 60 participants recruited from the community (lab-sourced). Crowdsourced participants responded to as many clips as they wanted and typed their responses, whereas lab-sourced participants gave spoken responses to 40 clips, and their responses were transcribed. The content of the responses was evaluated using a take-one-out procedure, which compared responses to other responses to the same clip and to other clips, with a comparison of the average number of shared words. In contrast to the 13 months of recruiting that was required to collect normative data from 60 lab-sourced participants (with specific demographic characteristics), only 34 days were needed to collect normative data from 99 crowdsourced participants (contributing a median of 22 responses). The majority of crowdsourced workers were female, and the median age was 35 years, lower than the lab-sourced median of 62 years but similar to the median age of the US population. The responses contributed by the crowdsourced participants were longer on average, that is, 33 words compared to 28 words (Pcrowdsourced participants had more shared words (P=.004 and .01 respectively), whereas younger participants had higher numbers of shared words in the lab-sourced population (P=.01). Crowdsourcing is an effective approach
and contained technological trajectories on a national level using a combination of methods from statistical natural language processing, vector space modelling and network analysis. The proposed approach does not aim at replacing the researcher or expert but rather offers the possibility to algorithmically...... in Denmark. Results show that in the explored case it is not mainly new technologies and applications that are driving change but innovative re-combinations of old and new technologies....
Large, David R; Clark, Leigh; Quandt, Annie; Burnett, Gary; Skrypchuk, Lee
Given the proliferation of 'intelligent' and 'socially-aware' digital assistants embodying everyday mobile technology - and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices - it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis - formulating responses; turn-taking; back
Toma, Irina; Brighiu, Stefan Mihai; Dascalu, Mihai; Trausan-Matu, Stefan
Learning a new language includes multiple aspects, from vocabulary acquisition to exercising words in sentences, and developing discourse building capabilities. In most learning scenarios, students learn individually and interact only during classes; therefore, it is difficult to enhance their
Full Text Available The paper deals with the problems of routing control algorithms of new conception of tram vehicle bogie. The main goal of these research activities is wear reduction of rail wheels and tracks, wear reduction of traction energy losses and increasing of running comfort. The testing experimental tram vehicle with special bogie construction powered by traction battery is utilized for these purposes. This vehicle has a rotary bogie with independent rotating wheels driven by permanent magnets synchronous motors and a solid axle. The wheel forces in bogie are measured by large amounts of the various sensors placed on the testing experimental tram vehicle. Nowadays the designed control algorithms are implemented to the vehicle superset control system. The traction requirements and track characteristics have an effect to these control algorithms. This control including sophisticated routing brings other improvements which is verified and corrected according to individual traction and driving characteristics, and opens new possibilities.
Dependency distance: A new perspective on the syntactic development in second language acquisition. Comment on "Dependency distance: A new perspective on syntactic patterns in natural language" by Haitao Liu et al.
Jiang, Jingyang; Ouyang, Jinghui
Liu et al.  offers a clear and informative account of the use of dependency distance in studying natural languages, with a focus on the viewpoint that dependency distance minimization (DDM) can be regarded as a linguistic universal. We would like to add the perspective of employing dependency distance in the studies of second languages acquisition (SLA), particularly the studies of syntactic development.
Smith, Sam; Sufi, Shoaib; Goble, Carole; Buchan, Iain
Background Data discovery, particularly the discovery of key variables and their inter-relationships, is key to secondary data analysis, and in-turn, the evolving field of data science. Interface designers have presumed that their users are domain experts, and so they have provided complex interfaces to support these “experts.” Such interfaces hark back to a time when searches needed to be accurate first time as there was a high computational cost associated with each search. Our work is part of a governmental research initiative between the medical and social research funding bodies to improve the use of social data in medical research. Objective The cross-disciplinary nature of data science can make no assumptions regarding the domain expertise of a particular scientist, whose interests may intersect multiple domains. Here we consider the common requirement for scientists to seek archived data for secondary analysis. This has more in common with search needs of the “Google generation” than with their single-domain, single-tool forebears. Our study compares a Google-like interface with traditional ways of searching for noncomplex health data in a data archive. Methods Two user interfaces are evaluated for the same set of tasks in extracting data from surveys stored in the UK Data Archive (UKDA). One interface, Web search, is “Google-like,” enabling users to browse, search for, and view metadata about study variables, whereas the other, traditional search, has standard multioption user interface. Results Using a comprehensive set of tasks with 20 volunteers, we found that the Web search interface met data discovery needs and expectations better than the traditional search. A task × interface repeated measures analysis showed a main effect indicating that answers found through the Web search interface were more likely to be correct (F 1,19=37.3, Peffect of task (F 3,57=6.3, Pinterface (F 1,19=18.0, Peffect of task (F 2,38=4.1, P=.025, Greenhouse
Cormican, Elin J.; Cormican, John D.
English language study should be introduced into the social work curriculum since various social judgments people make about each other on the basis of dialectal differences may interfere with communication between social workers and their clients, coworkers, or the general community. (Author/LBH)
Szathmáry, Eörs; Szathmáry, Zoltán; Ittzés, Péter; Orbaán, Geroő; Zachár, István; Huszár, Ferenc; Fedor, Anna; Varga, Máté; Számadó, Szabolcs
It is justified to assume that part of our genetic endowment contributes to our language skills, yet it is impossible to tell at this moment exactly how genes affect the language faculty. We complement experimental biological studies by an in silico approach in that we simulate the evolution of neuronal networks under selection for language-related skills. At the heart of this project is the Evolutionary Neurogenetic Algorithm (ENGA) that is deliberately biomimetic. The design of the system was inspired by important biological phenomena such as brain ontogenesis, neuron morphologies, and indirect genetic encoding. Neuronal networks were selected and were allowed to reproduce as a function of their performance in the given task. The selected neuronal networks in all scenarios were able to solve the communication problem they had to face. The most striking feature of the model is that it works with highly indirect genetic encoding--just as brains do.
This paper discusses the relevance of the discovery of mirror neurons in monkeys and of the mirror neuron system in humans to a neuroscientific account of primates' social cognition and its evolution. It is proposed that mirror neurons and the functional mechanism they underpin, embodied simulation, can ground within a unitary neurophysiological explanatory framework important aspects of human social cognition. In particular, the main focus is on language, here conceived according to a neurophenomenological perspective, grounding meaning on the social experience of action. A neurophysiological hypothesis--the "neural exploitation hypothesis"--is introduced to explain how key aspects of human social cognition are underpinned by brain mechanisms originally evolved for sensorimotor integration. It is proposed that these mechanisms were later on adapted as new neurofunctional architecture for thought and language, while retaining their original functions as well. By neural exploitation, social cognition and language can be linked to the experiential domain of action.
Harbusch, Karin; Itsova, Gergana; Koch, Ulrich; Kuhner, Christine
We built a natural language processing (NLP) system implementing a "virtual writing conference" for elementary-school children, with German as the target language. Currently, state-of-the-art computer support for writing tasks is restricted to multiple-choice questions or quizzes because automatic parsing of the often ambiguous and fragmentary…
Ingo, G. M.; Riccucci, C.; Faraldi, F.; Pascucci, M.; Messina, E.; Fierro, G.; Di Carlo, G.
By means of the combined use of X-ray photoelectron spectroscopy (XPS), optical microscopy (OM) and scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDS) the surface and subsurface chemical and metallurgical features of silver counterfeited Roman Republican coins are investigated to decipher some aspects of the manufacturing methods and to evaluate the technological ability of the Roman metallurgists to produce thin silver coatings. The results demonstrate that over 2000 ago important advances in the technology of thin layer deposition on metal substrates were attained by Romans. The ancient metallurgists produced counterfeited coins by combining sophisticated micro-plating methods and tailored surface chemical modification based on the mercury-silvering process. The results reveal that Romans were able systematically to chemically and metallurgically manipulate alloys at a micro scale to produce adherent precious metal layers with a uniform thickness up to few micrometers. The results converge to reveal that the production of forgeries was aimed firstly to save expensive metals as much as possible allowing profitable large-scale production at a lower cost. The driving forces could have been a lack of precious metals, an unexpected need to circulate coins for trade and/or a combinations of social, political and economic factors that requested a change in money supply. Finally, some information on corrosion products have been achieved useful to select materials and methods for the conservation of these important witnesses of technology and economy.
de Sá, Fábio P; Zina, Juliana; Haddad, Célio F B
Intraspecific communication in frogs plays an important role in the recognition of conspecifics in general and of potential rivals or mates in particular and therefore with relevant consequences for pre-zygotic reproductive isolation. We investigate intraspecific communication in Hylodes japi, an endemic Brazilian torrent frog with territorial males and an elaborate courtship behavior. We describe its repertoire of acoustic signals as well as one of the most complex repertoires of visual displays known in anurans, including five new visual displays. Previously unknown in frogs, we also describe a bimodal inter-sexual communication system where the female stimulates the male to emit a courtship call. As another novelty for frogs, we show that in addition to choosing which limb to signal with, males choose which of their two vocal sacs will be used for visual signaling. We explain how and why this is accomplished. Control of inflation also provides additional evidence that vocal sac movement and color must be important for visual communication, even while producing sound. Through the current knowledge on visual signaling in Neotropical torrent frogs (i.e. hylodids), we discuss and highlight the behavioral diversity in the family Hylodidae. Our findings indicate that communication in species of Hylodes is undoubtedly more sophisticated than we expected and that visual communication in anurans is more widespread than previously thought. This is especially true in tropical regions, most likely due to the higher number of species and phylogenetic groups and/or to ecological factors, such as higher microhabitat diversity.
Fábio P de Sá
Full Text Available Intraspecific communication in frogs plays an important role in the recognition of conspecifics in general and of potential rivals or mates in particular and therefore with relevant consequences for pre-zygotic reproductive isolation. We investigate intraspecific communication in Hylodes japi, an endemic Brazilian torrent frog with territorial males and an elaborate courtship behavior. We describe its repertoire of acoustic signals as well as one of the most complex repertoires of visual displays known in anurans, including five new visual displays. Previously unknown in frogs, we also describe a bimodal inter-sexual communication system where the female stimulates the male to emit a courtship call. As another novelty for frogs, we show that in addition to choosing which limb to signal with, males choose which of their two vocal sacs will be used for visual signaling. We explain how and why this is accomplished. Control of inflation also provides additional evidence that vocal sac movement and color must be important for visual communication, even while producing sound. Through the current knowledge on visual signaling in Neotropical torrent frogs (i.e. hylodids, we discuss and highlight the behavioral diversity in the family Hylodidae. Our findings indicate that communication in species of Hylodes is undoubtedly more sophisticated than we expected and that visual communication in anurans is more widespread than previously thought. This is especially true in tropical regions, most likely due to the higher number of species and phylogenetic groups and/or to ecological factors, such as higher microhabitat diversity.
Dautriche, Isabelle; Fibla, Laia; Fievet, Anne-Caroline; Christophe, Anne
Even though ambiguous words are common in languages, children find it hard to learn homophones, where a single label applies to several distinct meanings (e.g., Mazzocco, 1997). The present work addresses this apparent discrepancy between learning abilities and typological pattern, with respect to homophony in the lexicon. In a series of five experiments, 20-month-old French children easily learnt a pair of homophones if the two meanings associated with the phonological form belonged to different syntactic categories, or to different semantic categories. However, toddlers failed to learn homophones when the two meanings were distinguished only by different grammatical genders. In parallel, we analyzed the lexicon of four languages, Dutch, English, French and German, and observed that homophones are distributed non-arbitrarily in the lexicon, such that easily learnable homophones are more frequent than hard-to-learn ones: pairs of homophones are preferentially distributed across syntactic and semantic categories, but not across grammatical gender. We show that learning homophones is easier than previously thought, at least when the meanings of the same phonological form are made sufficiently distinct by their syntactic or semantic context. Following this, we propose that this learnability advantage translates into the overall structure of the lexicon, i.e., the kinds of homophones present in languages exhibit the properties that make them learnable by toddlers, thus allowing them to remain in languages. Copyright © 2018 Elsevier Inc. All rights reserved.
Larsen, Søren Støvelbæk; Fihl, Preben; Moeslund, Thomas B.
The purpose of this technical report is to cover the implementation of the Danish language and grammar in the Angus2 software. This includes a brief description of the Angus2 software, and the Danish grammar with relevance to the implementation in Angus2, and detailed description of how...
van Deemter, Kees; Krahmer, Emiel; Theune, Mariet
This paper challenges the received wisdom that template-based approaches to the generation of language are necessarily inferior to other approaches as regards their maintainability, linguistic well-foundedness and quality of output. Some recent NLG systems that call themselves `templatebased' will
Pascual Cantos Gomez
Full Text Available This paper ainis at presenting a survey of computational linguistic tools presently available but whose potential has been neither fully considered not exploited to its full in modern CALL. It starts with a discussion on the rationale of DDL to language learning, presenting typical DDL-activities. DDL-software and potential extensions of non-typical DDL-software (electronic dictionaries and electronic dictionary facilities to DDL . An extended section is devoted to describe NLP-technology and how it can be integrated into CALL, within already existing software or as stand alone resources. A range of NLP-tools is presentcd (MT programs, taggers, lemn~atizersp, arsers and speech technologies with special emphasis on tagged concordancing. The paper finishes with a number of reflections and ideas on how language technologies can be used efficiently within the language learning context and how extensive exploration and integration of these technologies might change and extend both modern CAI,I, and the present language learning paradigiii..
Harbusch, Karin; Itsova, Gergana; Koch, Ulrich; Kuhner, Christine
We built an NLP system implementing a "virtual writing conference" for elementary-school children, with German as the target language. Currently, state-of-the-art computer support for writing tasks is restricted to multiple-choice questions or quizzes because automatic parsing of the often ambiguous and fragmentary texts produced by pupils…
Recent research has produced evidence to suggest a strong reciprocal link between school context-specific language constructions that reflect a school's vision and schoolwide pedagogy, and the way that meaning making occurs, and a school's culture is characterized. This research was conducted within three diverse settings: one school in the Sydney…
Canfield, Caitlin F.; Edelson, Lisa R.; Saudino, Kimberly J.
Although the phenotypic correlation between language and nonverbal cognitive ability is well-documented, studies examining the etiology of the covariance between these abilities are scant, particularly in very young children. The goal of this study was to address this gap in the literature by examining the genetic and environmental links between…
Simpson, Sean S; Adams, Nikki; Brugman, Claudia M; Conners, Thomas J
With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were
Liu et al.  provide a comprehensive account of research on dependency distance in human languages. While the article is a very rich and useful report on this complex subject, here I will expand on a few specific issues where research in computational linguistics (specifically natural language processing) can inform DDM research, and vice versa. These aspects have not been explored much in  or elsewhere, probably due to the little overlap between both research communities, but they may provide interesting insights for improving our understanding of the evolution of human languages, the mechanisms by which the brain processes and understands language, and the construction of effective computer systems to achieve this goal.
Roblyer, S.P.; Owzarski, P.C.
The N-Reactor confinement system release dose to the public in a postulated accident is reduced by washing the confinement atmosphere with fog sprays. This allows a low pressure release of confinement atmosphere containing fission products through filters and out an elevated stack. The current accident analysis required revision of the CORRAL code and other codes such as CONTEMPT to properly model the N Reactor confinement into a system of multiple fog-sprayed compartments. In revising these codes, more sophisticated models for the fog sprays and iodine plateout were incorporated to remove some of the conservatism of steam condensing rate, fission product washout and iodine plateout than used in previous studies. The CORRAL code, which was used to describe the transport and deposition of airborne fission products in LWR containment systems for the Rasmussen Study, was revised to describe fog spray removal of molecular iodine (I 2 ) and particulates in multiple compartments for sprays having individual characteristics of on-off times, flow rates, fall heights, and drop sizes in changing containment atmospheres. During postulated accidents, the code determined the fission product removal rates internally rather than from input decontamination factors. A discussion is given of how the calculated plateout and washout rates vary with time throughout the analysis. The results of the accident analyses indicated that more credit could be given to fission product washout and plateout. An important finding was that the release of fission products to the atmosphere and adsorption of fission products on the filters were significantly lower than previous studies had indicated
Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.
The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.
Full Text Available The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.
Hornick, Thomas; Cohen, William; Miller, Glenn
The proposal selection process for the Hubble Space Telescope is assisted by a robust and easy to use query program (TACOS). The system parses an English subset language sentence regardless of the order of the keyword phases, allowing the user a greater flexibility than a standard command query language. Capabilities for macro and procedure definition are also integrated. The system was designed for flexibility in both use and maintenance. In addition, TACOS can be applied to any knowledge domain that can be expressed in terms of a single reaction. The system was implemented mostly in Common LISP. The TACOS design is described in detail, with particular attention given to the implementation methods of sentence processing.
re~arded as -a fairly complete dictionary contains about 18,000 itemsw at soluition to the domain-restricted task at tzanlating present, and will be... dictionary access and so on, with an article. Unfortunately, the Weidner system did but as time goes on, one might imagine functionality not know that...superfast type. looped tht it A31l be built with taste by peo. writer ought to be possible in the monolingual case pie who understand languages and
Silver, Steven S.
FMS/3 is a system for producing hard copy documentation at high speed from free format text and command input. The system was originally written in assembler language for a 12K IBM 360 model 20 using a high speed 1403 printer with the UCS-TN chain option (upper and lower case). Input was from an IBM 2560 Multi-function Card Machine. The model 20…
The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf ’ s law. This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf’s law and are then used to evaluate many of the theoretical explanations of Zipf’s law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf’s law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data. PMID:24664880
Francis Jeffry Pelletier
Full Text Available Strawson (1959 described ‘descriptive metaphysics’, Bach (1986a described ‘natural language metaphysics’, Sapir (1929 and Whorf (1940a,b, 1941 describe, well, Sapir-Whorﬁanism. And there are other views concerning the relation between correct semantic analysis of linguistic phenomena and the “reality” that is supposed to be thereby described. I think some considerations from the analyses of the mass-count distinction can shed some light on that very dark topic.ReferencesBach, Emmon. 1986a. ‘Natural Language Metaphysics’. In Ruth Barcan Marcus, G.J.W. Dorn & Paul Weingartner (eds. ‘Logic, Methodology, and Philosophy of Science, VII’, 573–595. Amsterdam: North Holland.Bach, Emmon. 1986b. ‘The Algebra of Events’. Linguistics and Philosophy 9: 5–16.Berger, Peter & Luckmann, Thomas. 1966. The Social Construction of Reality: A Treatise in the Sociology of Knowledge. New York: Doubleday.Boroditsky, Lera, Schmidt, Lauren & Phillips, Webb. 2003. ‘Sex, Syntax, and Semantics’. In Dedre Gentner & Susan Goldin-Meadow (eds. ‘Language in Mind: Advances in the Study of Language and Cognition’, 59–80. Cambridge, MA: MIT Press.Cheng, L. & Sybesma, R. 1999. ‘Bare and Not-So-Bare Nouns and the structure of NP’. Linguistic Inquiry 30: 509–542.http://dx.doi.org/10.1162/002438999554192Chierchia, Gennaro. 1998a. ‘Reference to Kinds across Languages’. Natural Language Semantics 6: 339–405.http://dx.doi.org/10.1023/A:1008324218506Chierchia, Gennaro. 1998b. ‘Plurality of Mass Nouns and the Notion of ‘Semantic Parameter’ ’. In S. Rothstein (ed. ‘Events and Grammar’, 53–103. Dordrecht: Kluwer.Chierchia, Gennaro. 2010. ‘Mass Nouns, Vagueness and Semantic Variation’. Synthèse 174: 99–149.http://dx.doi.org/10.1007/s11229-009-9686-6Doetjes, Jenny. 1997. Quantifiers and Selection: On the Distribution of Quantifying Expressions in French, Dutch and English. Ph.D. thesis, University of Leiden, Holland
Oyamada, Kiyoshi; Ikeda, Takao
This study developed minute estimation by adopting comprehensive sensitivity analytical program for reliability of TRU waste repository concepts in a crystalline rock condition. We examined each components and groundwater scenario of geological repository and prepared systematic bases to examine the reliability from the point of comprehensiveness. Models and data are sophisticated to examine the reliability. Based on an existing TRU waste repository concepts, effects of parameters to nuclide migration were quantitatively classified. Those parameters, that will be decided quantitatively, are such as site character of natural barrier and design specification of engineered barriers. Considering the feasibility of those figures of specifications, reliability is re-examined on combinations of those parameters within a practical range. Future issues are; Comprehensive representation of hybrid geosphere model including the fractured medium and permeable matrix medium. Sophistication of tools to develop the reliable combinations of parameters. It is significant to continue this study because the disposal concepts and specification of TRU nuclides containing waste on various sites shall be determined rationally and safely through these studies. (author)
Stevens, Kimberly A; Ronan, Prof Kevin; Davies, Gene
This paper reports on a new family-centred, feedback-informed intervention focused on evaluating therapeutic outcomes and language changes across treatment for conduct disorder (CD). The study included 26 youth and families from a larger randomised, controlled trial (Ronan et al., in preparation). Outcome measures reflected family functioning/youth compliance, delinquency, and family goal attainment. First- and last-treatment session audio files were transcribed into more than 286,000 words and evaluated through the Linguistic Inquiry and Word Count Analysis program (Pennebaker et al., 2007). Significant outcomes across family functioning/youth compliance, delinquency, goal attainment and word usage reflected moderate-strong effect sizes. Benchmarking findings also revealed reduced time of treatment delivery compared to a gold standard approach. Linguistic analysis revealed specific language changes across treatment. For caregivers, increased first person, action-oriented, present tense, and assent type words and decreased sadness words were found; for youth, significant reduction in use of leisure words. This study is the first using lexical analyses of natural language to assess change across treatment for conduct disordered youth and families. Such findings provided strong support for program tenets; others, more speculative support. Copyright © 2016. Published by Elsevier B.V.
Full Text Available When we examine the empirical investigations of motivation in second and foreign language learning, even those drawing upon the latest theoretical paradigms, such as the L2 motivational self system (Dörnyei, 2009, it becomes clear that many of them still fail to take account of its dynamic character and temporal variation. This may be surprising in view of the fact that the need to adopt such a process-oriented approach has been emphasized by a number of theorists and researchers (e.g., Dörnyei, 2000, 2001, 2009; Ushioda, 1996; Williams & Burden, 1997, and it lies at the heart of the model of second language motivation proposed by Dörnyei and Ottó (1998. It is also unfortunate that few research projects have addressed the question of how motivation changes during a language lesson as well as a series of lessons, and what factors might be responsible for fluctuations of this kind. The present paper is aimed to rectify this problem by reporting the findings of a classroom-based study which investigated the changes in the motivation of 28 senior high school students, both in terms of their goals and intentions, and their interest and engagement in classroom activities and tasks over the period of four weeks. The analysis of the data collected by means of questionnaires, observations and interviews showed that although the reasons for learning remain relatively stable, the intensity of motivation is indeed subject to variation on a minute-to-minute basis and this fact has to be recognized even in large-scale, cross-sectional research in this area.
Full Text Available Abstract Background Accurate information is needed to direct healthcare systems’ efforts to control methicillin-resistant Staphylococcus aureus (MRSA. Assembling complete and correct microbiology data is vital to understanding and addressing the multiple drug-resistant organisms in our hospitals. Methods Herein, we describe a system that securely gathers microbiology data from the Department of Veterans Affairs (VA network of databases. Using natural language processing methods, we applied an information extraction process to extract organisms and susceptibilities from the free-text data. We then validated the extraction against independently derived electronic data and expert annotation. Results We estimate that the collected microbiology data are 98.5% complete and that methicillin-resistant Staphylococcus aureus was extracted accurately 99.7% of the time. Conclusions Applying natural language processing methods to microbiology records appears to be a promising way to extract accurate and useful nosocomial pathogen surveillance data. Both scientific inquiry and the data’s reliability will be dependent on the surveillance system’s capability to compare from multiple sources and circumvent systematic error. The dataset constructed and methods used for this investigation could contribute to a comprehensive infectious disease surveillance system or other pressing needs.
Noguera-Arnaldos, José Ángel
The Internet of Things (IoT) offers opportunities for new applications and services that enable users to access and control their working and home environment from local and remote locations, aiming to perform daily life activities in an easy way. However, the IoT also introduces new challenges, some of which arise from the large range of devices currently available and the heterogeneous interfaces provided for their control. The control and management of this variety of devices and interfaces represent a new challenge for non-expert users, instead of making their life easier. Based on this understanding, in this work we present a natural language interface for the IoT, which takes advantage of Semantic Web technologies to allow non-expert users to control their home environment through an instant messaging application in an easy and intuitive way. We conducted several experiments with a group of end users aiming to evaluate the effectiveness of our approach to control home appliances by means of natural language instructions. The evaluation results proved that without the need for technicalities, the user was able to control the home appliances in an efficient way.
Hu, Weiming; Tian, Guodong; Kang, Yongxin; Yuan, Chunfeng; Maybank, Stephen
In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequence of atomic activities, the action represented by the trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.
Dethlefs, Nina; Milders, Maarten; Cuayáhuitl, Heriberto; Al-Salkini, Turkey; Douglas, Lorraine
Currently, an estimated 36 million people worldwide are affected by Alzheimer's disease or related dementias. In the absence of a cure, non-pharmacological interventions, such as cognitive stimulation, which slow down the rate of deterioration can benefit people with dementia and their caregivers. Such interventions have shown to improve well-being and slow down the rate of cognitive decline. It has further been shown that cognitive stimulation in interaction with a computer is as effective as with a human. However, the need to operate a computer often represents a difficulty for the elderly and stands in the way of widespread adoption. A possible solution to this obstacle is to provide a spoken natural language interface that allows people with dementia to interact with the cognitive stimulation software in the same way as they would interact with a human caregiver. This makes the assistive technology accessible to users regardless of their technical skills and provides a fully intuitive user experience. This article describes a pilot study that evaluated the feasibility of computer-based cognitive stimulation through a spoken natural language interface. Prototype software was evaluated with 23 users, including healthy elderly people and people with dementia. Feedback was overwhelmingly positive.
The traditional theory equating the brain bases of language with Broca's and Wernicke's neocortical areas is wrong. Neural circuits linking activity in anatomically segregated populations of neurons in subcortical structures and the neocortex throughout the human brain regulate complex behaviors such as walking, talking, and comprehending the meaning of sentences. When we hear or read a word, neural structures involved in the perception or real-world associations of the word are activated as well as posterior cortical regions adjacent to Wernicke's area. Many areas of the neocortex and subcortical structures support the cortical-striatal-cortical circuits that confer complex syntactic ability, speech production, and a large vocabulary. However, many of these structures also form part of the neural circuits regulating other aspects of behavior. For example, the basal ganglia, which regulate motor control, are also crucial elements in the circuits that confer human linguistic ability and abstract reasoning. The cerebellum, traditionally associated with motor control, is active in motor learning. The basal ganglia are also key elements in reward-based learning. Data from studies of Broca's aphasia, Parkinson's disease, hypoxia, focal brain damage, and a genetically transmitted brain anomaly (the putative "language gene," family KE), and from comparative studies of the brains and behavior of other species, demonstrate that the basal ganglia sequence the discrete elements that constitute a complete motor act, syntactic process, or thought process. Imaging studies of intact human subjects and electrophysiologic and tracer studies of the brains and behavior of other species confirm these findings. As Dobzansky put it, "Nothing in biology makes sense except in the light of evolution" (cited in Mayr, 1982). That applies with as much force to the human brain and the neural bases of language as it does to the human foot or jaw. The converse follows: the mark of evolution on
easily transformed into a regrettable mistake (don’t cry over spilt milk ) if G is not characterized as a fleeting goal and a recovery plan therefore...technical literature is characterized by very dry and literal language. If there is one place where metaphors might not intrude, it must be when people...from the point of view of both evidential support and falsification ? I ask it because you didn’t say anything about it. A: Well, I think there’s a lot
In response to the need for reliable results from natural language processing, this book presents an original way of decomposing a language(s) in a microscopic manner by means of intra/inter‑language norms and divergences, going progressively from languages as systems to the linguistic, mathematical and computational models, which being based on a constructive approach are inherently traceable. Languages are described with their elements aggregating or repelling each other to form viable interrelated micro‑systems. The abstract model, which contrary to the current state of the art works in int
Full Text Available According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray and 13 subgroups using two well-known methods: Support Vector Machine (SVM and K-Nearest Neighbor (KNN. The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system due to common features characterizing these subclasses. The overall results of the study were successful.
Johns, Brendan T; Jamieson, Randall K
The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, ; Jones & Mewhort, ; Landauer & Dumais, ; Mikolov, Sutskever, Chen, Corrado, & Dean, ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing. Copyright © 2018 Cognitive Science Society, Inc.
Vlas, Radu Eduard
Open source projects do have requirements; they are, however, mostly informal, text descriptions found in requests, forums, and other correspondence. Understanding such requirements provides insight into the nature of open source projects. Unfortunately, manual analysis of natural language requirements is time-consuming, and for large projects,…
Eichhorn, G.; Accomazzi, A.; Grant, C. S.; Henneken, E.; Kurtz, M. J.; Murray, S. S.
The ADS provides access to over 940,000 references from astronomy and planetary sciences publications and 1.5 million records from physics publications. It is funded by NASA and provides free access to these references, as well as to 2.4 million scanned pages from the astronomical literature. These include most of the major astronomy and several planetary sciences journals, as well as many historical observatory publications. The references now include the abstracts from all volumes of the Journal of Geophysical Research (JGR) since the beginning of 2002. We get these abstracts on a regular basis. The Kluwer journal Solar Physics has been scanned back to volume 1 and is available through the ADS. We have extracted the reference lists from this and many other journals and included them in the reference and citation database of the ADS. We have recently scanning Earth, Moon and Planets, another Kluwer journal, and will scan other Kluwer journals in the future as well. We plan on extracting references from these journals as well in the near future. The ADS has many sophisticated query features. These allow the user to formulate complex queries. Using results lists to get further information about the selected articles provide the means to quickly find important and relevant articles from the database. Three advanced feedback queries are available from the bottom of the ADS results list (in addition to regular feedback queries already available from the abstract page and from the bottom of the results list): 1. Get reference list for selected articles: This query returns all known references for the selected articles (or for all articles in the first list). The resulting list will be ranked according to how often each article is referred to and will show the most referenced articles in the field of study that created the first list. It presumably shows the most important articles in that field. 2. Get citation list for selected articles: This returns all known articles
Full Text Available The aim of this study were to know semantic meaning of predicate Ngajengan, Daharan, Ngelor, Mangan, Ngrodok (Eating, Kaken (Eating, Suap, Bejijit, (Eating Bekeruak (Eating, Ngerasak (Eating and Nyangklok (Eating. Besides that, to know the lexical meaning of each words and the function of words in every sentences especially the meaning of eating in Sasaknese language. The lexical meaning of Ngajengan, Daharan, Ngelor, Mangan, Ngrodok (Eating, Kaken (Eating, Suap, Bejijit, (Eating Bekeruak (Eating, Ngerasak (Eating and Nyangklok (Eating was doing something to eat but the differences of these words are usage in sentences. Besides that, the word usage based on the subject and object and there is predicate that need tool to state eat meals or food.
Toledo, Sue Walker; Davis, Bruce
This paper will discuss a two and a half year long project undertaken to develop an English-language interface for the geographical information system GRASS. The work was carried out for NASA by a small business, Netrologic, based in San Diego, California, under Phase 1 and 2 Small Business Innovative Research contracts. We consider here the potential value of this system whose current functionality addresses numerical, categorical and boolean raster layers and includes the display of point sets defined by constraints on one or more layers, answers yes/no and numerical questions, and creates statistical reports. It also handles complex queries and lexical ambiguities, and allows temporarily switching to UNIX or GRASS.
Exploring language universal is one of the major goals of linguistic researches, which are largely devoted to answering the ;Platonic questions; in linguistics, that is, what is the language knowledge, how to get and use this knowledge. However, if solely guided by linguistic intuition, it is very difficult for syntactic studies to answer these questions, or to achieve abstractions in the scientific sense. This suggests that linguistic analyses based on the probability theory may provide effective ways to investigate into language universals in terms of biological motivations or cognitive psychological mechanisms. With the view that ;Language is a human-driven system;, Liu, Xu & Liang's review  pointed out that dependency distance minimization (DDM), which has been corroborated by big data analysis of corpus, may be a language universal shaped in language evolution, a universal that has profound effect on syntactic patterns.
Trivedi, Hari; Mesterhazy, Joseph; Laguna, Benjamin; Vu, Thienkhai; Sohn, Jae Ho
Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader's contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was
In this paper, we investigate the effectiveness of a CRF-based learning method for identifying necessary Web of Things (WoT) application components that would satisfy the users' requests issued in natural language. For instance, a user request such as "archive all sports breaking news" can be satisfied by composing a WoT application that consists of ESPN breaking news service and Dropbox as a storage service. We built an engine that can identify the necessary application components by recognizing a main act (MA) or named entities (NEs) from a given request. We trained this engine with the descriptions of WoT applications (called recipes) that were collected from IFTTT WoT platform. IFTTT hosts over 300 WoT entities that offer thousands of functions referred to as triggers and actions. There are more than 270,000 publicly-available recipes composed with those functions by real users. Therefore, the set of these recipes is well-qualified for the training of our MA and NE recognition engine. We share our unique experience of generating the training and test set from these recipe descriptions and assess the performance of the CRF-based language method. Based on the performance evaluation, we introduce further research directions.
Doan, Son; Maehara, Cleo K; Chaparro, Juan D; Lu, Sisi; Liu, Ruiling; Graham, Amanda; Berry, Erika; Hsu, Chun-Nan; Kanegaye, John T; Lloyd, David D; Ohno-Machado, Lucila; Burns, Jane C; Tremoulet, Adriana H
Delayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) patients for whom the diagnosis of KD should be considered. We developed an NLP tool that recognizes the KD diagnostic criteria based on standard clinical terms and medical word usage using 22 pediatric ED notes augmented by Unified Medical Language System vocabulary. With high suspicion for KD defined as fever and three or more KD clinical signs, KD-NLP was applied to 253 ED notes from children ultimately diagnosed with either KD or another febrile illness. We evaluated KD-NLP performance against ED notes manually reviewed by clinicians and compared the results to a simple keyword search. KD-NLP identified high-suspicion patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians. The tool outperformed a simple keyword search (sensitivity = 41.0%; specificity = 76.3%). KD-NLP showed comparable performance to clinician manual chart review for identification of pediatric ED patients with a high suspicion for KD. This tool could be incorporated into the ED electronic health record system to alert providers to consider the diagnosis of KD. KD-NLP could serve as a model for decision support for other conditions in the ED. © 2016 by the Society for Academic Emergency Medicine.
With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. In this context, a polyphonic model of discourse derived from Bakhtin’s work as a paradigm is used for analyzing both general texts and CSCL conversations in a unique framework focused on different facets of textual cohesion. As specificity of our analysis, the individual learning perspective is focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model, whereas the collaborative learning dimension is centered on the evaluation of participants’ involvement, as well as on collaboration assessment. Our approach based on advanced Natural Language Processing techniques provides a qualitative estimation of the learning process and enhance...
He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo
Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.
Karakülah, Gökhan; Dicle, Oğuz; Koşaner, Ozgün; Suner, Aslı; Birant, Çağdaş Can; Berber, Tolga; Canbek, Sezin
The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.
Cohen, K. Bretonnel; Xia, Jingbo; Roeder, Christophe; Hunter, Lawrence E.
There is currently a crisis in science related to highly publicized failures to reproduce large numbers of published studies. The current work proposes, by way of case studies, a methodology for moving the study of reproducibility in computational work to a full stage beyond that of earlier work. Specifically, it presents a case study in attempting to reproduce the reports of two R libraries for doing text mining of the PubMed/MEDLINE repository of scientific publications. The main findings are that a rational paradigm for reproduction of natural language processing papers can be established; the advertised functionality was difficult, but not impossible, to reproduce; and reproducibility studies can produce additional insights into the functioning of the published system. Additionally, the work on reproducibility lead to the production of novel user-centered documentation that has been accessed 260 times since its publication—an average of once a day per library. PMID:29568821
Sada, Yvonne; Hou, Jason; Richardson, Peter; El-Serag, Hashem; Davila, Jessica
Accurate identification of hepatocellular cancer (HCC) cases from automated data is needed for efficient and valid quality improvement initiatives and research. We validated HCC International Classification of Diseases, 9th Revision (ICD-9) codes, and evaluated whether natural language processing by the Automated Retrieval Console (ARC) for document classification improves HCC identification. We identified a cohort of patients with ICD-9 codes for HCC during 2005-2010 from Veterans Affairs administrative data. Pathology and radiology reports were reviewed to confirm HCC. The positive predictive value (PPV), sensitivity, and specificity of ICD-9 codes were calculated. A split validation study of pathology and radiology reports was performed to develop and validate ARC algorithms. Reports were manually classified as diagnostic of HCC or not. ARC generated document classification algorithms using the Clinical Text Analysis and Knowledge Extraction System. ARC performance was compared with manual classification. PPV, sensitivity, and specificity of ARC were calculated. A total of 1138 patients with HCC were identified by ICD-9 codes. On the basis of manual review, 773 had HCC. The HCC ICD-9 code algorithm had a PPV of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, we identified 471 pathology reports for 323 patients and 943 radiology reports for 557 patients. The pathology ARC algorithm had PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. A combined approach of ICD-9 codes and natural language processing of pathology and radiology reports improves HCC case identification in automated data.
Mubin, O.; Bartneck, C.; Feijs, L.M.G.
The project described hereunder focuses on the design and implementation of a "Artificial Robotic Interaction Language", where the research goal is to find a balance between the effort necessary from the user to learn a new language and the resulting benefit of optimized automatic speech recognition
Pahisa Solé, Joan
En aquesta tesi doctoral, presentem un sistema de compansió que transforma el llenguatge telegràfic (frases formades per paraules de contingut no flexionades), derivat de la comunicació augmentativa i alternativa (CAA) basada en pictogrames, a llenguatge natural en català i en castellà. El sistema ha sigut dissenyat per millorar la comunicació de persones usuàries de CAA que habitualment tenen greus problemes a la parla, així com problemes motrius, i que utilitzen mètodes de comunicació basat...
Klein, J C; Slama, G
We have conceived a truly pre-programmable infusion system usable for intravenous administration of insulin in diabetic subjects. The original system has been built into a small, commercially available, syringe-pump of which only the case and the mechanical parts have been kept. The computing until has a timer, a programmable memory of 512 words by 8 bits and a digital-to-frequency converter to run the motor which drives the syringe. The memory contains 8 profiles of insulin injections stored in digital form over 64 words. Each profile is selected by the patient before eating according to the carbohydrate content of the planned meal and last about two hours, starting from and returning to the basal rate of insulin, at which it remains until next profile selection. Amount, profiles and duration of insulin injection are either mean values deduced from previous studies with a closed-loop artificial pancreas or personally fitted values; they are stored in an instantly replaceable memory cell. This device allows the patient to choose the time, nature and amount of his food intake.
Liu, Bingli; Chen, Xinying
In the target article , Liu et al. provide an informative introduction to the dependency distance studies and proclaim that language syntactic patterns, that relate to the dependency distance, are associated with human cognitive mechanisms, such as limited working memory and syntax processing. Therefore, such syntactic patterns are probably 'human-driven' language universals. Sufficient evidence based on big data analysis is also given in the article for supporting this idea. The hypotheses generally seem very convincing yet still need further tests from various perspectives. Diachronic linguistic study based on authentic language data, on our opinion, can be one of those 'further tests'.
Kalogeropoulos, A.; Albæk, E.; de Vreese, C.H.; van Dalen, A.
In analogy to political sophistication, it is imperative that citizens have a certain level of economic sophistication, especially in times of heated debates about the economy. This study examines the impact of different influences (media, interpersonal communication and personal experiences) on
Blair, Kristine L.
With the recent interest in the fifth century B.C. theories of Protagoras and Gorgias come assumptions about the philosophical affinity of the Greek educator Isocrates to this pair of older sophists. Isocratean education in discourse, with its emphasis on collaborative political discourse, falls within recent definitions of a sophist curriculum.…
Allen, James E.
While Aristotle's philosophical views are more foundational than those of many of the Older Sophists, Aristotle's rhetorical theories inherit and incorporate many of the central tenets ascribed to Sophistic rhetoric, albeit in a more systematic fashion, as represented in the "Rhetoric." However, Aristotle was more than just a rhetorical…
Holism in interwar Germany provides an excellent example for social and political influences on scientific developments. Deeply impressed by the ubiquitous invocation of a cultural crisis, biologists, physicians, and psychologists presented holistic accounts as an alternative to the "mechanistic worldview" of the nineteenth century. Although the ideological background of these accounts is often blatantly obvious, many holistic scientists did not content themselves with a general opposition to a mechanistic worldview but aimed at a rational foundation of their holistic projects. This article will discuss the work of Kurt Goldstein, who is known for both his groundbreaking contributions to neuropsychology and his holistic philosophy of human nature. By focusing on Goldstein's neurolinguistic research, I want to reconstruct the empirical foundations of his holistic program without ignoring its cultural background. In this sense, Goldstein's work provides a case study for the formation of a scientific theory through the complex interplay between specific empirical evidences and the general cultural developments of the Weimar Republic. © 2012 Wiley Periodicals, Inc.
Full Text Available Abstract Background The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyon's Hôpital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED medical records. The first step consists of developing an application (UrgIndex which automatically extracts and encodes information found in narrative reports. The purpose of the present article is to describe and evaluate this natural language processing system. Methods Narrative reports have to be pre-processed before utilizing the French-language medical multi-terminology indexer (ECMT for standardized encoding. UrgIndex identifies and excludes syntagmas containing a negation and replaces non-standard terms (abbreviations, acronyms, spelling errors.... Then, the phrases are sent to the ECMT through an Internet connection. The indexer's reply, based on Extensible Markup Language, returns codes and literals corresponding to the concepts found in phrases. UrgIndex filters codes corresponding to suspected infections. Recall is defined as the number of relevant processed medical concepts divided by the number of concepts evaluated (coded manually by the medical epidemiologist. Precision is defined as the number of relevant processed concepts divided by the number of concepts proposed by UrgIndex. Recall and precision were assessed for respiratory and cutaneous syndromes. Results Evaluation of 1,674 processed medical concepts contained in 100 ED medical records (50 for respiratory syndromes and 50 for cutaneous syndromes showed an overall recall of 85.8% (95% CI: 84.1-87.3. Recall varied from 84.5% for respiratory syndromes to 87.0% for cutaneous syndromes. The
Harvard Univ., Cambridge, MA. Graduate School of Education.
THIS BIBLIOGRAPHY LISTS MATERIAL ON VARIOUS ASPECTS OF LANGUAGE DEVELOPMENT. APPROXIMATELY 65 UNANNOTATED REFERENCES ARE PROVIDED TO DOCUMENTS DATING FROM 1958 TO 1966. JOURNALS, BOOKS, AND REPORT MATERIALS ARE LISTED. SUBJECT AREAS INCLUDED ARE THE NATURE OF LANGUAGE, LINGUISTICS, LANGUAGE LEARNING, LANGUAGE SKILLS, LANGUAGE PATTERNS, AND…
Kumar, Rajesh; Yunus, Reva
This article looks at the contribution of insights from theoretical linguistics to an understanding of language acquisition and the nature of language in terms of their potential benefit to language education. We examine the ideas of innateness and universal language faculty, as well as multilingualism and the language-society relationship. Modern…
Dominick, Wayne D. (Editor); Liu, I-Hsiung
This Working Paper Series entry represents a collection of presentation visuals associated with the companion report entitled Natural Language Query System Design for Interactive Information Storage and Retrieval Systems, USL/DBMS NASA/RECON Working Paper Series report number DBMS.NASA/RECON-17.
This research thesis aimed at the development of a natural-language-based user-friendly interface for the searching of relational data bases. The author first addresses how to store data which will be accessible through an interface in natural language: this organisation must result in as few constraints as possible in query formulation. He briefly presents techniques related to the automatic processing of natural language, and highlights the need for a more user-friendly interface. Then, he presents the developed interface and outlines the user-friendliness and ergonomics of implemented procedures. He shows how the interface has been designed to deliver information and explanations on its processing. This allows the user to control the relevance of the answer. He also indicates the classification of mistakes and errors which may be present in queries in natural language. He finally gives an overview of possible evolutions of the interface, briefly presents deductive functionalities which could expand data management. The handling of complex objects is also addressed [fr
This interesting and informative review by Liu and colleagues  in this issue covers the full spectrum of research on the idea that in natural language, dependency distance tends to be small. The authors discuss two distinct research threads: experimental work from psycholinguistics on online processes in comprehension and production, and text-corpus studies of dependency length distributions.
D'Mello, Sidney K; Dowell, Nia; Graesser, Arthur
There is the question of whether learning differs when students speak versus type their responses when interacting with intelligent tutoring systems with natural language dialogues. Theoretical bases exist for three contrasting hypotheses. The speech facilitation hypothesis predicts that spoken input will increase learning, whereas the text facilitation hypothesis predicts typed input will be superior. The modality equivalence hypothesis claims that learning gains will be equivalent. Previous experiments that tested these hypotheses were confounded by automated speech recognition systems with substantial error rates that were detected by learners. We addressed this concern in two experiments via a Wizard of Oz procedure, where a human intercepted the learner's speech and transcribed the utterances before submitting them to the tutor. The overall pattern of the results supported the following conclusions: (1) learning gains associated with spoken and typed input were on par and quantitatively higher than a no-intervention control, (2) participants' evaluations of the session were not influenced by modality, and (3) there were no modality effects associated with differences in prior knowledge and typing proficiency. Although the results generally support the modality equivalence hypothesis, highly motivated learners reported lower cognitive load and demonstrated increased learning when typing compared with speaking. We discuss the implications of our findings for intelligent tutoring systems that can support typed and spoken input.
Bail, Christopher Andrew
Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.
Fong, Allan; Harriott, Nicole; Walters, Donna M; Foley, Hanan; Morrissey, Richard; Ratwani, Raj R
Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports. Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared. Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process. We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis. Copyright © 2017 Elsevier B.V. All rights reserved.
Full Text Available Web of Things (WoT platforms are growing fast so as the needs for composing WoT apps more easily and efficiently. We have recently commenced the campaign to develop an interface where users can issue requests for WoT apps entirely in natural language. This requires an effort to build a system that can learn to identify relevant WoT functions that fulfill user's requests. In our preceding work, we trained a supervised learning system with thousands of publicly-available IFTTT app recipes based on conditional random fields (CRF. However, the sub-par accuracy and excessive training time motivated us to devise a better approach. In this paper, we present a novel solution that creates a separate learning engine for each trigger service. With this approach, parallel and incremental learning becomes possible. For inference, our system first identifies the most relevant trigger service for a given user request by using an information retrieval technique. Then, the learning engine associated with the trigger service predicts the most likely pair of trigger and action functions. We expect that such two-phase inference method given parallel learning engines would improve the accuracy of identifying related WoT functions. We verify our new solution through the empirical evaluation with training and test sets sampled from a pool of refined IFTTT app recipes. We also meticulously analyze the characteristics of the recipes to find future research directions.
Kimia, Amir A; Savova, Guergana; Landschaft, Assaf; Harper, Marvin B
Electronically stored clinical documents may contain both structured data and unstructured data. The use of structured clinical data varies by facility, but clinicians are familiar with coded data such as International Classification of Diseases, Ninth Revision, Systematized Nomenclature of Medicine-Clinical Terms codes, and commonly other data including patient chief complaints or laboratory results. Most electronic health records have much more clinical information stored as unstructured data, for example, clinical narrative such as history of present illness, procedure notes, and clinical decision making are stored as unstructured data. Despite the importance of this information, electronic capture or retrieval of unstructured clinical data has been challenging. The field of natural language processing (NLP) is undergoing rapid development, and existing tools can be successfully used for quality improvement, research, healthcare coding, and even billing compliance. In this brief review, we provide examples of successful uses of NLP using emergency medicine physician visit notes for various projects and the challenges of retrieving specific data and finally present practical methods that can run on a standard personal computer as well as high-end state-of-the-art funded processes run by leading NLP informatics researchers.
Full Text Available When using assistive systems, the consideration of individual and cultural meaning is crucial for the utility and acceptance of technology. Orientation, communication and interaction are rooted in perception and therefore always happen in material space. We understand that a major problem lies in the difference between human and technical perception of space. Cultural policies are based on meanings including their spatial situation and their rich relationships. Therefore, we have developed an approach where the different perception systems share a hybrid spatial model that is generated by artificial intelligence—a joint effort by humans and assistive systems. The aim of our project is to create a spatial model of cultural meaning based on interaction between humans and robots. We define the role of humanoid robots as becoming our companions. This calls for technical systems to include still inconceivable human and cultural agendas for the perception of space. In two experiments, we tested a first prototype of the communication module that allows a humanoid to learn cultural meanings through a machine learning system. Interaction is achieved by non-verbal and natural-language communication between humanoids and test persons. This helps us to better understand how a spatial model of cultural meaning can be developed.
Web of Things (WoT) platforms are growing fast so as the needs for composing WoT apps more easily and efficiently. We have recently commenced the campaign to develop an interface where users can issue requests for WoT apps entirely in natural language. This requires an effort to build a system that can learn to identify relevant WoT functions that fulfill user's requests. In our preceding work, we trained a supervised learning system with thousands of publicly-available IFTTT app recipes based on conditional random fields (CRF). However, the sub-par accuracy and excessive training time motivated us to devise a better approach. In this paper, we present a novel solution that creates a separate learning engine for each trigger service. With this approach, parallel and incremental learning becomes possible. For inference, our system first identifies the most relevant trigger service for a given user request by using an information retrieval technique. Then, the learning engine associated with the trigger service predicts the most likely pair of trigger and action functions. We expect that such two-phase inference method given parallel learning engines would improve the accuracy of identifying related WoT functions. We verify our new solution through the empirical evaluation with training and test sets sampled from a pool of refined IFTTT app recipes. We also meticulously analyze the characteristics of the recipes to find future research directions.
Shepherd, Debra Lynne
The regional and cultural closeness of Botswana and South Africa, as well as differences in their political histories and language policy stances, offers a unique opportunity to evaluate the role of language in reading outcomes. This study aims to empirically test the effect of exposure to mother tongue and English instruction on the reading…
Botting, Nicola; Gaynor, Marguerite; Tucker, Katie; Orchard-Lisle, Ginnie
Some reports suggest that there is an increase in the number of children identified as having developmental language impairment (Bercow, 2008). yet resource issues have meant that many speech and language therapy services have compromised provision in some way. Thus, efficient ways of identifying need and prioritizing intervention are required.…
Meystre, Stéphane M; Lee, Sanghoon; Jung, Chai Young; Chevrier, Raphaël D
An increasing need for collaboration and resources sharing in the Natural Language Processing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology for all information annotated and extracted from clinical text. We have combined two existing standards: the HL7 Clinical Document Architecture (CDA), and the ISO Graph Annotation Format (GrAF; in development), to develop such a data model entitled "CDA+GrAF". We experimented with several methods to combine these existing standards, and eventually selected a method wrapping separate CDA and GrAF parts in a common standoff annotation (i.e., separate from the annotated text) XML document. Two use cases, clinical document sections, and the 2010 i2b2/VA NLP Challenge (i.e., problems, tests, and treatments, with their assertions and relations), were used to create examples of such standoff annotation documents, and were successfully validated with the XML schemata provided with both standards. We developed a tool to automatically translate annotation documents from the 2010 i2b2/VA NLP Challenge format to GrAF, and automatically generated 50 annotation documents using this tool, all successfully validated. Finally, we adapted the XSL stylesheet provided with HL7 CDA to allow viewing annotation XML documents in a web browser, and plan to adapt existing tools for translating annotation documents between CDA+GrAF and the UIMA and GATE frameworks. This common data model may ease directly comparing NLP tools and applications, combining their output, transforming and "translating" annotations between different NLP applications, and eventually "plug-and-play" of different modules in NLP applications. Copyright © 2011 Elsevier Inc. All rights reserved.
Grundmeier, Robert W; Masino, Aaron J; Casper, T Charles; Dean, Jonathan M; Bell, Jamie; Enriquez, Rene; Deakyne, Sara; Chamberlain, James M; Alpern, Elizabeth R
Important information to support healthcare quality improvement is often recorded in free text documents such as radiology reports. Natural language processing (NLP) methods may help extract this information, but these methods have rarely been applied outside the research laboratories where they were developed. To implement and validate NLP tools to identify long bone fractures for pediatric emergency medicine quality improvement. Using freely available statistical software packages, we implemented NLP methods to identify long bone fractures from radiology reports. A sample of 1,000 radiology reports was used to construct three candidate classification models. A test set of 500 reports was used to validate the model performance. Blinded manual review of radiology reports by two independent physicians provided the reference standard. Each radiology report was segmented and word stem and bigram features were constructed. Common English "stop words" and rare features were excluded. We used 10-fold cross-validation to select optimal configuration parameters for each model. Accuracy, recall, precision and the F1 score were calculated. The final model was compared to the use of diagnosis codes for the identification of patients with long bone fractures. There were 329 unique word stems and 344 bigrams in the training documents. A support vector machine classifier with Gaussian kernel performed best on the test set with accuracy=0.958, recall=0.969, precision=0.940, and F1 score=0.954. Optimal parameters for this model were cost=4 and gamma=0.005. The three classification models that we tested all performed better than diagnosis codes in terms of accuracy, precision, and F1 score (diagnosis code accuracy=0.932, recall=0.960, precision=0.896, and F1 score=0.927). NLP methods using a corpus of 1,000 training documents accurately identified acute long bone fractures from radiology reports. Strategic use of straightforward NLP methods, implemented with freely available
Schroeck, Florian R; Patterson, Olga V; Alba, Patrick R; Pattison, Erik A; Seigne, John D; DuVall, Scott L; Robertson, Douglas J; Sirovich, Brenda; Goodney, Philip P
To take the first step toward assembling population-based cohorts of patients with bladder cancer with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full-text pathology reports. Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence vs absence and depth), grade, the presence of muscularis propria, and the presence of carcinoma in situ. Our gold standard was based on an independent review of reports by 2 urologists, followed by adjudication. We assessed the NLP performance by calculating the accuracy, the positive predictive value, and the sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 patients with bladder cancer. When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for the presence vs the absence of carcinoma in situ. Accuracy for histology, invasion (presence vs absence), grade, and the presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with an acceptable positive predictive value for lamina propria (0.82) and for muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of the patients with bladder cancer. NLP had high accuracy for 5 of 6 variables and abstracted data for the vast majority of the patients. This now allows for the assembly of population-based cohorts with longitudinal pathology data. Published by Elsevier Inc.
Ferraro, Jeffrey P; Daumé, Hal; Duvall, Scott L; Chapman, Wendy W; Harkema, Henk; Haug, Peter J
Natural language processing (NLP) tasks are commonly decomposed into subtasks, chained together to form processing pipelines. The residual error produced in these subtasks propagates, adversely affecting the end objectives. Limited availability of annotated clinical data remains a barrier to reaching state-of-the-art operating characteristics using statistically based NLP tools in the clinical domain. Here we explore the unique linguistic constructions of clinical texts and demonstrate the loss in operating characteristics when out-of-the-box part-of-speech (POS) tagging tools are applied to the clinical domain. We test a domain adaptation approach integrating a novel lexical-generation probability rule used in a transformation-based learner to boost POS performance on clinical narratives. Two target corpora from independent healthcare institutions were constructed from high frequency clinical narratives. Four leading POS taggers with their out-of-the-box models trained from general English and biomedical abstracts were evaluated against these clinical corpora. A high performing domain adaptation method, Easy Adapt, was compared to our newly proposed method ClinAdapt. The evaluated POS taggers drop in accuracy by 8.5-15% when tested on clinical narratives. The highest performing tagger reports an accuracy of 88.6%. Domain adaptation with Easy Adapt reports accuracies of 88.3-91.0% on clinical texts. ClinAdapt reports 93.2-93.9%. ClinAdapt successfully boosts POS tagging performance through domain adaptation requiring a modest amount of annotated clinical data. Improving the performance of critical NLP subtasks is expected to reduce pipeline error propagation leading to better overall results on complex processing tasks.
Lendholt, Matthias; Hammitzsch, Martin; Wächter, Joachim
The Common Alerting Protocol (CAP)  is an XML-based data format for exchanging public warnings and emergencies between alerting technologies. In conjunction with the Emergency Data Exchange Language (EDXL) Distribution Element (-DE)  these data formats can be used for warning message dissemination in early warning systems for natural hazards. Application took place in the DEWS (Distance Early Warning System)  project where CAP serves as central message format containing both human readable warnings and structured data for automatic processing by message receivers. In particular the spatial reference capabilities are of paramount importance both in CAP and EDXL. Affected areas are addressable via geo codes like HASC (Hierarchical Administrative Subdivision Codes)  or UN/LOCODE  but also with arbitrary polygons that can be directly generated out of GML . For each affected area standardized criticality values (urgency, severity and certainty) have to be set but also application specific key-value-pairs like estimated time of arrival or maximum inundation height can be specified. This enables - together with multilingualism, message aggregation and message conversion for different dissemination channels - the generation of user-specific tailored warning messages.  CAP, http://www.oasis-emergency.org/cap  EDXL-DE, http://docs.oasis-open.org/emergency/edxl-de/v1.0/EDXL-DE_Spec_v1.0.pdf  DEWS, http://www.dews-online.org  HASC, "Administrative Subdivisions of Countries: A Comprehensive World Reference, 1900 Through 1998" ISBN 0-7864-0729-8  UN/LOCODE, http://www.unece.org/cefact/codesfortrade/codes_index.htm  GML, http://www.opengeospatial.org/standards/gml
The Hippocratic treatise The Art is an epideictic speech in defence of medicine against certain unnamed detractors. The author of The Art is fully aware of the fact that for him, language (as opposed to, say, a live demonstration) is the medium of education. Accordingly, the author shows full command of the main issues of the late fifth century 'sophistic' debate on the nature and the correct and effective use of language. In his views on language, the author seems to adopt a quite positivistic stance. For him, words reflect our perception and interpretation of the visual appearances or eidea of the things that are, and these appearances prove the existence of things in nature. To this extent, language reflects reality, provided that we language users have the expertise to form correct interpretations of what we observe. At the same time, language remains a secondary phenomenon: it is not a 'growth' of nature, but a set of conventional signs that have a basis in reality only if they are applied correctly. There is always the possibility of incorrect interpretation of our perceptions, which will lead to an incorrect use of language that does not reflect real phenomena. Words remain conventional expressions, and not all words can be expected to reflect the truth. In fact, the unnamed detractors of the art are victim to many such incorrect interpretations. Consistent with his view of language as secondary to visual phenomena, the author claims in his peroration that as a medium for the defence of medicine, the spoken word is generally considered less effective than live demonstrations. This modesty, while undoubtedly effective as a means to catch the sympathy of his public, still seems slightly overstated. Our author is fully aware of the powers and limitations of his medium, and shows great sophistication in its use.
Topaz, Maxim; Lai, Kenneth; Dowding, Dawn; Lei, Victor J; Zisberg, Anna; Bowles, Kathryn H; Zhou, Li
Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care. This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes. First, two human annotators manually reviewed a purposeful training sample (n=360) and random test sample (n=1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data. The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system's accuracy=92.7%), with best results for wound treatment (F-measure=95.7%) and poorest results for wound size (F-measure=81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis. The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data. Copyright Â© 2016 Elsevier Ltd. All
This research thesis reports the development of a user-friendly interface in natural language for querying a relational database. The developed system differs from usual approaches for its integrated architecture as the relational model management is totally controlled by the interface. The author first addresses the way to store data in order to make them accessible through an interface in natural language, and more precisely to store data with an organisation which would result in the less possible constraints in query formulation. The author then briefly presents techniques related to automatic processing in natural language, and discusses the implications of a better user-friendliness and for error processing. The next part reports the study of the developed interface: selection of data processing tools, interface development, data management at the interface level, information input by the user. The last chapter proposes an overview of possible evolutions for the interface: use of deductive functionalities, use of an extensional base and of an intentional base to deduce facts from knowledge stores in the extensional base, and handling of complex objects [fr
Verspoor, Karin; Cohen, Kevin Bretonnel; Lanfranchi, Arrick; Warner, Colin; Johnson, Helen L; Roeder, Christophe; Choi, Jinho D; Funk, Christopher; Malenkiy, Yuriy; Eckert, Miriam; Xue, Nianwen; Baumgartner, William A; Bada, Michael; Palmer, Martha; Hunter, Lawrence E
We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications.
Zimmerer, V. C.; Varley, R. A.
Processing of linear word order (linear configuration) is important for virtually all languages and essential to languages such as English which have little functional morphology. Damage to systems underpinning configurational processing may specifically affect word-order reliant sentence structures. We explore order processing in WR, a man with primary progressive aphasia (PPA). In a previous report, we showed how WR showed impaired processing of actives, which rely strongly on word order, b...
Zhai, Haijun; Lingren, Todd; Deleger, Louise; Li, Qi; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre
A high-quality gold standard is vital for supervised, machine learning-based, clinical natural language processing (NLP) systems. In clinical NLP projects, expert annotators traditionally create the gold standard. However, traditional annotation is expensive and time-consuming. To reduce the cost of annotation, general NLP projects have turned to crowdsourcing based on Web 2.0 technology, which involves submitting smaller subtasks to a coordinated marketplace of workers on the Internet. Many studies have been conducted in the area of crowdsourcing, but only a few have focused on tasks in the general NLP field and only a handful in the biomedical domain, usually based upon very small pilot sample sizes. In addition, the quality of the crowdsourced biomedical NLP corpora were never exceptional when compared to traditionally-developed gold standards. The previously reported results on medical named entity annotation task showed a 0.68 F-measure based agreement between crowdsourced and traditionally-developed corpora. Building upon previous work from the general crowdsourcing research, this study investigated the usability of crowdsourcing in the clinical NLP domain with special emphasis on achieving high agreement between crowdsourced and traditionally-developed corpora. To build the gold standard for evaluating the crowdsourcing workers' performance, 1042 clinical trial announcements (CTAs) from the ClinicalTrials.gov website were randomly selected and double annotated for medication names, medication types, and linked attributes. For the experiments, we used CrowdFlower, an Amazon Mechanical Turk-based crowdsourcing platform. We calculated sensitivity, precision, and F-measure to evaluate the quality of the crowd's work and tested the statistical significance (Pcrowdsourced and traditionally-developed annotations. The agreement between the crowd's annotations and the traditionally-generated corpora was high for: (1) annotations (0.87, F-measure for medication names
Political attitudes can be associated with moral concerns. This research investigated whether people's level of political sophistication moderates this association. Based on the Moral Foundations Theory, this article examined whether political sophistication moderates the extent to which reliance on moral foundations, as categories of moral concerns, predicts judgements about policy positions. With this aim, two studies examined four policy positions shown by previous research to be best predicted by the endorsement of Sanctity, that is, the category of moral concerns focused on the preservation of physical and spiritual purity. The results showed that reliance on Sanctity predicted political sophisticates' judgements, as opposed to those of unsophisticates, on policy positions dealing with equal rights for same-sex and unmarried couples and with euthanasia. Political sophistication also interacted with Fairness endorsement, which includes moral concerns for equal treatment of everybody and reciprocity, in predicting judgements about equal rights for unmarried couples, and interacted with reliance on Authority, which includes moral concerns for obedience and respect for traditional authorities, in predicting opposition to stem cell research. Those findings suggest that, at least for these particular issues, endorsement of moral foundations can be associated with political attitudes more strongly among sophisticates than unsophisticates. © 2015 International Union of Psychological Science.
Full Text Available Theory of Mind (ToM, i.e. the ability to understand others' mental states, endows humans with highly adaptive social skills such as teaching or deceiving. Candidate evolutionary explanations have been proposed for the unique sophistication of human ToM among primates. For example, the Machiavellian intelligence hypothesis states that the increasing complexity of social networks may have induced a demand for sophisticated ToM. This type of scenario ignores neurocognitive constraints that may eventually be crucial limiting factors for ToM evolution. In contradistinction, the cognitive scaffolding hypothesis asserts that a species' opportunity to develop sophisticated ToM is mostly determined by its general cognitive capacity (on which ToM is scaffolded. However, the actual relationships between ToM sophistication and either brain volume (a proxy for general cognitive capacity or social group size (a proxy for social network complexity are unclear. Here, we let 39 individuals sampled from seven non-human primate species (lemurs, macaques, mangabeys, orangutans, gorillas and chimpanzees engage in simple dyadic games against artificial ToM players (via a familiar human caregiver. Using computational analyses of primates' choice sequences, we found that the probability of exhibiting a ToM-compatible learning style is mainly driven by species' brain volume (rather than by social group size. Moreover, primates' social cognitive sophistication culminates in a precursor form of ToM, which still falls short of human fully-developed ToM abilities.
Bayan Abu Shawar
Full Text Available In this paper, we describe a way to access Arabic Web Question Answering (QA corpus using a chatbot, without the need for sophisticated natural language processing or logical inference. Any Natural Language (NL interface to Question Answer (QA system is constrained to reply with the given answers, so there is no need for NL generation to recreate well-formed answers, or for deep analysis or logical inference to map user input questions onto this logical ontology; simple (but large set of pattern-template matching rules will suffice. In previous research, this approach works properly with English and other European languages. In this paper, we try to see how the same chatbot will react in terms of Arabic Web QA corpus. Initial results shows that 93% of answers were correct, but because of a lot of characteristics related to Arabic language, changing Arabic questions into other forms may lead to no answers.
Rombouts, Jeroen V.K.; Stentoft, Lars; Violante, Francesco
innovation for a Laplace innovation assumption improves the pricing in a smaller way. Apart from investigating directly the value of model sophistication in terms of dollar losses, we also use the model condence set approach to statistically infer the set of models that delivers the best pricing performance.......We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differer...
Hebbelstrup Rye Rasmussen, Stig
This article examines the impact of cognitive ability on ideological voting. We find, using a US sample and a Danish sample, that the effect of cognitive ability rivals the effect of the traditionally strongest predicter of ideological voting political sophistication. Furthermore, the results...... are consistent with the effect of cognitive ability being partly mediated by political sophistication. Much of the effect of cognitive ability remains however and is not explained by differences in education or Openness to experience either. The implications of these results for democratic theory are discussed....
Gustafsson Sendén, Marie; Bäck, Emma A; Lindqvist, Anna
The implementation of gender fair language is often associated with negative reactions and hostile attacks on people who propose a change. This was also the case in Sweden in 2012 when a third gender-neutral pronoun hen was proposed as an addition to the already existing Swedish pronouns for she (hon) and he (han). The pronoun hen can be used both generically, when gender is unknown or irrelevant, and as a transgender pronoun for people who categorize themselves outside the gender dichotomy. In this article we review the process from 2012 to 2015. No other language has so far added a third gender-neutral pronoun, existing parallel with two gendered pronouns, that actually have reached the broader population of language users. This makes the situation in Sweden unique. We present data on attitudes toward hen during the past 4 years and analyze how time is associated with the attitudes in the process of introducing hen to the Swedish language. In 2012 the majority of the Swedish population was negative to the word, but already in 2014 there was a significant shift to more positive attitudes. Time was one of the strongest predictors for attitudes also when other relevant factors were controlled for. The actual use of the word also increased, although to a lesser extent than the attitudes shifted. We conclude that new words challenging the binary gender system evoke hostile and negative reactions, but also that attitudes can normalize rather quickly. We see this finding very positive and hope it could motivate language amendments and initiatives for gender-fair language, although the first responses may be negative.
Guimaraes, Carolina V; Grzeszczuk, Robert; Bisset, George S; Donnelly, Lane F
When implementing or monitoring department-sanctioned standardized radiology reports, feedback about individual faculty performance has been shown to be a useful driver of faculty compliance. Most commonly, these data are derived from manual audit, which can be both time-consuming and subject to sampling error. The purpose of this study was to evaluate whether a software program using natural language processing and machine learning could accurately audit radiologist compliance with the use of standardized reports compared with performed manual audits. Radiology reports from a 1-month period were loaded into such a software program, and faculty compliance with use of standardized reports was calculated. For that same period, manual audits were performed (25 reports audited for each of 42 faculty members). The mean compliance rates calculated by automated auditing were then compared with the confidence interval of the mean rate by manual audit. The mean compliance rate for use of standardized reports as determined by manual audit was 91.2% with a confidence interval between 89.3% and 92.8%. The mean compliance rate calculated by automated auditing was 92.0%, within that confidence interval. This study shows that by use of natural language processing and machine learning algorithms, an automated analysis can accurately define whether reports are compliant with use of standardized report templates and language, compared with manual audits. This may avoid significant labor costs related to conducting the manual auditing process. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.
Peter, Johannes; Rosman, Tom; Mayer, Anne-Kathrin; Leichner, Nikolas; Krampen, Günter
Background: Particularly in higher education, not only a view of science as a means of finding absolute truths (absolutism), but also a view of science as generally tentative (multiplicism) can be unsophisticated and obstructive for learning. Most quantitative epistemic belief inventories neglect this and understand epistemic sophistication as…
Full Text Available A strong link has been established between operational excellence and the degree of sophistication of logistics organization, a function of factors such as performance monitoring, investment in Information Technology [IT] and the formalization of logistics organization, as proposed in the Bowersox, Daugherty, Dröge, Germain and Rogers (1992 Leading Edge model. At the same time, shippers have been increasingly outsourcing their logistics activities to third party providers. This paper, based on a survey with large Brazilian shippers, addresses a gap in the literature by investigating the relationship between dimensions of logistics organization sophistication and drivers of logistics outsourcing. To this end, the dimensions behind the logistics sophistication construct were first investigated. Results from factor analysis led to the identification of six dimensions of logistics sophistication. By means of multivariate logistical regression analyses it was possible to relate some of these dimensions, such as the formalization of the logistics organization, to certain drivers of the outsourcing of logistics activities of Brazilian shippers, such as cost savings. These results indicate the possibility of segmenting shippers according to characteristics of their logistics organization, which may be particularly useful to logistics service providers.
Danckert, Bolette; Dinesen, Peter Thisted; Sønderskov, Kim Mannemar
is founded on politically sophisticated individuals having a greater comprehension of news and other mass-mediated sources, which makes them less likely to rely on neighborhood cues as sources of information relevant for political attitudes. Based on a unique panel data set with fine-grained information...
Scott, J. Blake
Claims that teaching ethics is particularly important to technical writing. Outlines a classical, sophistic approach to ethics based on the theories and pedagogies of Protagoras, Gorgias, and Isocrates, which emphasizes the Greek concept of "nomos," internal and external deliberation, and responsible action. Discusses problems and…
This article looks at the work of contemporary Dutch fashion designer Alexander van Slobbe (1959) and examines how, since the 1990s, his fashion practices have consistently and consciously put forward a unique reflection on questions related to materiality, sophisticated archaism, luxury,
This article looks at the work of contemporary Dutch fashion designer Alexander van Slobbe (1959) and examines how, since the 1990s, his fashion practices have consistently and consciously put forward a unique reflection on questions related to materiality, sophisticated archaism, luxury,
Full Text Available This longitudinal case study explored Iranian EFL learners’ lexical complexity (LC through the lenses of Dynamic Systems Theory (DST. Fifty independent essays written by five intermediate to advanced female EFL learners in a TOEFL iBT preparation course over six months constituted the corpus of this study. Three Coh-Metrix indices (Graesser, McNamara, Louwerse, & Cai, 2004; McNamara & Graesser, 2012, three Lexical Complexity Analyzer indices (Lu, 2010, 2012; Lu & Ai, 2011, and four Vocabprofile indices (Cobb, 2000 were selected to measure different dimensions of LC. Results of repeated measures analysis of variance (RM ANOVA indicated an improvement with regard to only lexical sophistication. Positive and significant relationships were found between time and mean values in Academic Word List and Beyond-2000 as indicators of lexical sophistication. The remaining seven indices of LC, falling short of significance, tended to flatten over the course of this writing program. Correlation analyses among LC indices indicated that lexical density enjoyed positive correlations with lexical sophistication. However, lexical diversity revealed no significant correlations with both lexical density and lexical sophistication. This study suggests that DST perspective specifies a viable foundation for analyzing lexical complexity
Ranasinghe, R.W.M.R.J.B.; Callaghan, D.; Roelvink, D.
The dependency between the accuracy/uncertainty of storm erosion exceedance estimates obtained via a probabilistic model and the level of sophistication of the structural function (storm erosion model) embedded in the probabilistic model is assessed via the application of Callaghan et al.'s (2008)
Three threshold hypotheses proposed by Cummins (1976) and Diaz (1985) as explanations of data on the cognitive consequences of bilingualism are examined in depth and compared to one another. A neuroscientifically updated information-processing perspective on the interaction of second-language comprehension and visual-processing ability is…
Marie eGustafsson Sendén
Full Text Available The implementation of gender fair language is often associated with negative reactions and hostile attack on people who propose a change. This was also the case in Sweden in 2012 when a third gender-neutral pronoun hen was proposed as an addition to the already existing Swedish pronouns for she and he. The pronoun hen can be used both generically, when gender is unknown or irrelevant, and as a transgender pronoun for people who categorize themselves outside the gender dichotomy. In this article we review the process from 2012 to 2015 when hen has been introduced in the Swedish Dictionary. No other language has so far added a third gender-neutral pronoun that actually has reached the broader population of language users, which makes the situation in Sweden unique. We present data on attitudes toward hen during the recent four years and study how time is associated with the attitudes. In 2012 the majority of the Swedish population was negative to the word, but already in 2014 there was a significant shift to more positive attitudes. Time was one of the strongest predictors for attitudes also when other relevant factors were controlled for. Even though to a lesser extent than the attitudes, the actual use of the word has also increased. We conclude that new words challenging the binary gender system evoke hostile and negative reactions, but also that attitudes can normalize rather quickly. This is very positive because it should motivate language amendments and initiatives for gender-fair language although the first responses are negative.
"First language acquisition" commonly means the acquisition of a single language in childhood, regardless of the number of languages in a child's natural environment. Language acquisition is variously viewed as predetermined, wondrous, a source of concern, and as developing through formal processes. "First language teaching" concerns schooling in…
Elías Gómez Macías
Full Text Available Partiendo de óxido de magnesio comercial se preparó una suspensión acuosa, la cual se secó y calcinó para conferirle estabilidad térmica. El material, tanto fresco como usado, se caracterizó mediante DRX, área superficial BET y SEM-EPMA. El catalizador mostró una matriz de MgO tipo periclasa con CaO en la superficie. Las pruebas de actividad catalítica se efectuaron en lecho fijo empacado con partículas obtenidas mediante prensado, trituración y clasificación del material. El flujo de reactivos consistió en mezclas gas natural-aire por debajo del límite inferior de inflamabilidad. Para diferentes flujos y temperaturas de entrada de la mezcla reactiva, se midieron las concentraciones de CH4, CO2 y CO en los gases de combustión con un analizador de gases tipo infrarrojo no dispersivo (NDIR. Para alcanzar conversión total de metano se requirió aumentar la temperatura de entrada al lecho a medida que se incrementó el flujo de gases reaccionantes. Los resultados obtenidos permiten desarrollar un sistema de combustión catalítica de bajo costo con un material térmicamente estable, que promueva la alta eficiencia en la combustión de gas natural y elimine los problemas de estabilidad, seguridad y de impacto ambiental negativo inherentes a los procesos de combustión térmica convencional.
language interface to these new capabilities as well as to the existing data bases and graphic display facilities. BBN is developing a series of...Action. Artificial Intelligence , 1986. Forthcoming. [Hinrichs 81] Hinrichs, E. Temporale Anaphora um Englischen. 1981. Unpublished ms., University of...organized by NIKL has been demonstrated for a wide variety of sentence types. Table 3 shows a series of independent sentences that Penman is now able
Full Text Available The backward-chaining inference strategy of Prolog is inefficient for a number of problems. The article proposes Contralog: a Prolog-conform, forward-chaining language and an inference engine that is implemented as a preprocessor-compiler to Prolog. The target model is Prolog, which ensures mutual switching from Contralog to Prolog and back. The Contralog compiler is implemented using Prolog's de facto standardized macro expansion capability. The article goes into details regarding the target model.
Full Text Available The world’s most numerous bird, the domestic chicken, and their wild ancestor, the red junglefowl, have long been used as model species for animal behaviour research. Recently, this research has advanced our understanding of the social behaviour, personality, and cognition of fowl, and demonstrated their sophisticated behaviour and cognitive skills. Here, we overview some of this research, starting with describing research investigating the well-developed senses of fowl, before presenting how socially and cognitively complex they can be. The realisation that domestic chickens, our most abundant production animal, are behaviourally and cognitively sophisticated should encourage an increase in general appraise and fascination towards them. In turn, this should inspire increased use of them as both research and hobby animals, as well as improvements in their unfortunately often poor welfare.
The paper analyses the advantages and disadvantages of the second generation improved technologies and third generation technologies mainly developed in China in terms of safety and economy. The paper also discusses the maturity of the second generation improved technologies and the sophistication of the third generation technologies respectively. Meanwhile, the paper proposes that the advantage and disadvantage of second generation improved technologies and third generation technologies should be carefully taken into consideration and the relationship between the maturity and sophistication should be properly dealt with in the current stage. A two-step strategy shall be taken as a solution to solve the problem of insufficient capacity of nuclear power, trace and develop the third generation technologies, so as to ensure the sound and fast development of nuclear power. (authors)
Paola Boel; Gabriele Camera
The welfare cost of anticipated inflation is quantified in a calibrated model of the U.S. economy that exhibits tractable equilibrium dispersion in wealth and earnings. Inflation does not generate large losses in societal welfare, yet its impact varies noticeably across segments of society depending also on the financial sophistication of the economy. If money is the only asset, then inflation hurts mostly the wealthier and more productive agents, while those poorer and less productive may ev...
Szénási, Lieutenant-Colonel Endre
According to my experiences, the Western world hopelessly fails to understand Russian mentality, or misinterprets it. During my analysis of the Russian way of thinking I devoted special attention to the examination of military mentality. I have connected the issue of the Russian way of thinking to the contemporary imperial policies of Putin’s Russia. I have also attempted to prove the level of sophistication of both. I hope that a better understanding of both the Russian mentality and imperi...
Oommen, Elizabeth R; McCarthy, John W
In childhood apraxia of speech (CAS), children exhibit varying levels of speech intelligibility depending on the nature of errors in articulation and prosody. Augmentative and alternative communication (AAC) strategies are beneficial, and commonly adopted with children with CAS. This study focused on the decision-making process and strategies adopted by speech-language pathologists (SLPs) when simultaneously implementing interventions that focused on natural speech and AAC. Eight SLPs, with significant clinical experience in CAS and AAC interventions, participated in an online focus group. Thematic analysis revealed eight themes: key decision-making factors; treatment history and rationale; benefits; challenges; therapy strategies and activities; collaboration with team members; recommendations; and other comments. Results are discussed along with clinical implications and directions for future research.
Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra
Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.
Cíntia Matsuda Toledo
Full Text Available Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age. In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM with a radial basis function (RBF kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS is a strong candidate to replace manual feature selection methods.
Patton, Desmond Upton; MacBeth, Jamie; Schoenebeck, Sarita; Shear, Katherine; McKeown, Kathleen
There is a dearth of research investigating youths’ experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. First, we conducted a close textual read on a sample of tweets by Gakirah Barnes, a gang-involved teenaged girl in Chicago, and members of her Twitter network, over a 19-day period in 2014 during which 2 significant deaths occurred: that of Raason “Lil B” Shaw and Gakirah’s own death. We leverage the grief literature to understand the way Gakirah and her peers express thoughts, feelings, and behaviors at the time of these deaths. We also present and explain the rich and complex style of online communication among gang-involved youth, one that has been overlooked in prior research. Next, we overview the natural language processing output for expressions of loss and grief in our data set based on qualitative findings and present an error analysis on its output for grief. We conclude with a call for interdisciplinary research that analyzes online and offline behaviors to help understand physical and emotional violence and other problematic behaviors prevalent among marginalized communities. PMID:29636619
Language modeling plays a critical role in natural language processing and understanding. Starting from a general structure, language models are able to learn natural language patterns from rich input data. However, the state-of-the-art language models only take advantage of words themselves, which
We derive two big-step abstract machines, a natural semantics, and the valuation function of a denotational semantics based on the small-step abstract machine for Core Scheme presented by Clinger at PLDI'98. Starting from a functional implementation of this small-step abstract machine, (1) we fuse...... its transition function with its driver loop, obtaining the functional implementation of a big-step abstract machine; (2) we adjust this big-step abstract machine so that it is in defunctionalized form, obtaining the functional implementation of a second big-step abstract machine; (3) we...... refunctionalize this adjusted abstract machine, obtaining the functional implementation of a natural semantics in continuation style; and (4) we closure-unconvert this natural semantics, obtaining a compositional continuation-passing evaluation function which we identify as the functional implementation...
Verbeeten, Frank H M
This study examines the impact of uncertainty on the sophistication of capital budgeting practices. While the theoretical applications of sophisticated capital budgeting practices (defined as the use of real option reasoning and/or game theory decision rules) have been well documented, empirical
Full Text Available This article analyses the three epideictic orations of Isocrates which are in themselves a precious testimony of the quality of intellectual life at the close of the fourth century before Christ. To this period belong also the Socratics who are generally seen as an important link between Socrates and Plato. The author of this article proposes a more productive approach to the study of Antisthenes, Euclid of Megara and other so-called Socratics, revealing them not as independent thinkers but rather as adherents of the sophistic school and also as teachers, thereby, including them among those who took part in the educative activity of their time
Mourier, J; Nonglaton, J M; Syratchev, I V; Tanner, L
CTF3 (CLIC Test Facility 3), currently under construction at CERN, is a test facility designed to demonstrate the key feasibility issues of the CLIC (Compact LInear Collider) two-beam scheme. When completed, this facility will consist of a 150 MeV linac followed by two rings for bunch-interleaving, and a test stand where 30 GHz power will be generated. In this paper, the work that has been carried out on the linac's low power RF system is described. This includes, in particular, a sophisticated phase control system for the RF pulse compressor to produce a flat-top rectangular pulse over 1.4 µs.
We derive two big-step abstract machines, a natural semantics, and the valuation function of a denotational semantics based on the small-step abstract machine for Core Scheme presented by Clinger at PLDI'98. Starting from a functional implementation of this small-step abstract machine, (1) we fus...
Full Text Available The paper is dedicated to the problem of determination of the essence of language union and language family in modern linguistics, which is considered important, because these terms are often used as absolute synonyms. The research is relevant due to the need to distinguish the features of languages that are inherited during their functioning within either language union or language family when these languages are compared. The research has been carried out in order to present the historical background of the problem and to justify the need for differentiation of language facts that allow relating languages to particular language union or language family. In order to fulfill the goal of this work, descriptive, comparative, and historical methods have been used. A range of examples has been provided to prove that some languages, mainly Slavonic and Baltic languages, form a language family rather than a language union, because a whole number of features in their systems are the heritage of their common Indo-European past. Firstly, it is necessary to take into account changes having either common or different nature in the system of particular languages; secondly, one must have a precise idea of what features in the phonetic and morphological systems of compared languages allow to relate them to language union or language family; thirdly, it must be determined whether the changes in compared languages are regular or of any other type. On the basis of the obtained results, the following conclusions have been drawn: language union and language family are two different types of relations between modern languages; they allow identifying both degree of similarity of these languages and causes of differences between them. It is most important that one should distinguish and describe the specific features of two basic groups of languages forming language family or language union. The results obtained during the analysis are very important for linguistics
Friedman, Carol; Rindflesch, Thomas C; Corn, Milton
Natural language processing (NLP) is crucial for advancing healthcare because it is needed to transform relevant information locked in text into structured data that can be used by computer processes aimed at improving patient care and advancing medicine. In light of the importance of NLP to health, the National Library of Medicine (NLM) recently sponsored a workshop to review the state of the art in NLP focusing on text in English, both in biomedicine and in the general language domain. Specific goals of the NLM-sponsored workshop were to identify the current state of the art, grand challenges and specific roadblocks, and to identify effective use and best practices. This paper reports on the main outcomes of the workshop, including an overview of the state of the art, strategies for advancing the field, and obstacles that need to be addressed, resulting in recommendations for a research agenda intended to advance the field. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
Safari, Leila; Patrick, Jon D
This paper reports on a generic framework to provide clinicians with the ability to conduct complex analyses on elaborate research topics using cascaded queries to resolve internal time-event dependencies in the research questions, as an extension to the proposed Clinical Data Analytics Language (CliniDAL). A cascaded query model is proposed to resolve internal time-event dependencies in the queries which can have up to five levels of criteria starting with a query to define subjects to be admitted into a study, followed by a query to define the time span of the experiment. Three more cascaded queries can be required to define control groups, control variables and output variables which all together simulate a real scientific experiment. According to the complexity of the research questions, the cascaded query model has the flexibility of merging some lower level queries for simple research questions or adding a nested query to each level to compose more complex queries. Three different scenarios (one of them contains two studies) are described and used for evaluation of the proposed solution. CliniDAL's complex analyses solution enables answering complex queries with time-event dependencies at most in a few hours which manually would take many days. An evaluation of results of the research studies based on the comparison between CliniDAL and SQL solutions reveals high usability and efficiency of CliniDAL's solution. Copyright © 2018 Elsevier Inc. All rights reserved.
Nelde, Peter Hans
Examines the phenomenon of language contact and recent trends in linguistic contact research, which focuses on language use, language users, and language spheres. Also discusses the role of linguistic and cultural conflicts in language contact situations. (13 references) (MDM)
V. P. Kotchnev
Full Text Available The paper looks at the teaching process at schools of the natural sciences profile. The subject of the research is devoted to the correlations between the students’ progress and the degree of their involvement in creative activities of problem solving in the natural sciences context. The research is aimed to demonstrate the reinforce- ment of students’ creative learning by teaching mathematical schemes and structures. The comparative characteristics of the task, problem and model approaches to mathematical problem solving are given; the experimental data on the efficiency of mathematical training based on the above approaches being discussed, as well as the specifics of modeling the tasks for problem solving. The author examines the ways for stimulating the students’ creative activity and motivating the knowledge acquisition, and search for the new mathematical conformities related to the natural science content. The significance of the Olympiad and other non-standard tasks, broadening the students’ horizons and stimulating creative thinking and abilities, is emphasized.The proposed method confirms the appropriateness of introducing the Olympiad and non-standard problem solving into the preparatory training curricula for the Unified State Examinations.
Hussey, Karen A.; Katz, Albert N.; Leith, Scott A.
Over two studies, we examined the nature of gendered language in interactive discourse. In the first study, we analyzed gendered language from a chat corpus to see whether tokens of gendered language proposed in the gender-as-culture hypothesis (Maltz and Borker in "Language and social identity." Cambridge University Press, Cambridge, pp…
Uses the philosophy of Taoism as a metaphor in describing the whole language approach to language arts instruction. The discussion covers the key principles that inform the whole language approach, the resulting holistic nature of language programs, and the role of the teacher in this approach. (16 references) (CLB)
Cowley, Stephen; Gahrn-Andersen, Rasmus
Building on a distributed perspective, the Special Issue develops Alain Berthoz's concept of simplexity. By so doing, neurophysiology is used to reach beyond observable and, specifically, 1st-order languaging. While simplexity clarifies how language uses perception/action, a community's ‘lexicon......’ (a linguistic 2nd order) also shapes human powers. People use global constraints to make and construe wordings and bring a social/individual duality to human living. Within a field of perception-action-language, the phenomenology of ‘words’ and ‘things’ drives people to sustain their own experience....... Simplex tricks used in building bodies co-function with action that grants humans access to en-natured culture where, together, they build human knowing....
J. Mannar Mannan; M. Sundarambal; S. Raghul
Semantic web is to extend the current human readable web to encoding some of the semantic of resources in a machine processing form. As a Semantic web component, Semantic Web Services (SWS) uses a mark-up that makes the data into detailed and sophisticated machine readable way. One such language is Ontology Web Language (OWL). Existing conventional web service annotation can be changed to semantic web service by mapping Web Service Description Language (WSDL) with the semantic annotation of O...
Full Text Available Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue.
Full Text Available Background: Constructing gene co-expression networks from cancer expression data is important for investigating the genetic mechanisms underlying cancer. However, correlation coefficients or linear regression models are not able to model sophisticated relationships among gene expression profiles. Here, we address the 3-way interaction that 2 genes’ expression levels are clustered in different space locations under the control of a third gene’s expression levels. Results: We present xSyn, a software tool for identifying such 3-way interactions from cancer gene expression data based on an optimization procedure involving the usage of UPGMA (Unweighted Pair Group Method with Arithmetic Mean and synergy. The effectiveness is demonstrated by application to 2 real gene expression data sets. Conclusions: xSyn is a useful tool for decoding the complex relationships among gene expression profiles. xSyn is available at http://www.bdxconsult.com/xSyn.html .
Loyau, Adeline; Blanchet, Simon; van Laere, Pauline; Clobert, Jean; Danchin, Etienne
Semen limitation (lack of semen to fertilize all of a female's eggs) imposes high fitness costs to female partners. Females should therefore avoid mating with semen-limited males. This can be achieved by using public information extracted from watching individual males' previous copulating activities. This adaptive preference should be flexible given that semen limitation is temporary. We first demonstrate that the number of offspring produced by males Drosophila melanogaster gradually decreases over successive copulations. We then show that females avoid mating with males they just watched copulating and that visual public cues are sufficient to elicit this response. Finally, after males were given the time to replenish their sperm reserves, females did not avoid the males they previously saw copulating anymore. These results suggest that female fruit flies may have evolved sophisticated behavioural processes of resistance to semen-limited males, and demonstrate unsuspected adaptive context-dependent mate choice in an invertebrate.
Noack, M.; Seybold, J.; Ruehle, R.
The software environment RSYST was originally used to solve problems of reactor physics. The consideration of advanced scientific simulation requirements and the strict application of modern software design principles led to a system which is perfectly suitable to solve problems in various complex scientific problem domains. Starting with a review of the early days of RSYST, we describe the straight evolution driven by the need of software environment which combines the advantages of a high-performance database system with the capability to integrate sophisticated scientific technical applications. The RSYST architecture is presented and the data modelling capabilities are described. To demonstrate the powerful possibilities and flexibility of the RSYST environment, we describe a wide range of RSYST applications, e.g., mechanical simulations of multibody systems, which are used in biomechanical research, civil engineering and robotics. In addition, a hypermedia system which is used for scientific technical training and documentation is presented. (orig.) [de
A global introduction to language technology and the areas of computer science where language technology plays a role. Surveyed in this volume are issueas related to the parsing problem in the fields of natural languages, programming languages, and formal languages. Throughout the book attention is
Hall, Robert A., Jr.
This text focuses on the nature of language learning in the light of modern linguistic analysis. Common linguistic problems encountered by students of eight major languages are examined--Latin, Greek, French, Spanish, Portuguese, Italian, German, and Russian. The text discusses the nature of language, building new language habits, overcoming…
Bal, H.E.; Steiner, J.G.; Tanenbaum, A.S.
When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less
Neuroimaging techniques are becoming not only more and more sophisticated but are also coming to be increasingly accessible to researchers. One thing that one should take note of is the potential of neuroimaging research within second language acquisition (SLA) to contribute to issues pertaining to the plasticity of the adult brain and to general…
Gong, Tao; Shuai, Lan
Memory is essential to many cognitive tasks including language. Apart from empirical studies of memory effects on language acquisition and use, there lack sufficient evolutionary explorations on whether a high level of memory capacity is prerequisite for language and whether language origin could influence memory capacity. In line with evolutionary theories that natural selection refined language-related cognitive abilities, we advocated a coevolution scenario between language and memory capacity, which incorporated the genetic transmission of individual memory capacity, cultural transmission of idiolects, and natural and cultural selections on individual reproduction and language teaching. To illustrate the coevolution dynamics, we adopted a multi-agent computational model simulating the emergence of lexical items and simple syntax through iterated communications. Simulations showed that: along with the origin of a communal language, an initially-low memory capacity for acquired linguistic knowledge was boosted; and such coherent increase in linguistic understandability and memory capacities reflected a language-memory coevolution; and such coevolution stopped till memory capacities became sufficient for language communications. Statistical analyses revealed that the coevolution was realized mainly by natural selection based on individual communicative success in cultural transmissions. This work elaborated the biology-culture parallelism of language evolution, demonstrated the driving force of culturally-constituted factors for natural selection of individual cognitive abilities, and suggested that the degree difference in language-related cognitive abilities between humans and nonhuman animals could result from a coevolution with language. PMID:26544876
Stojkoski, Viktor; Utkovski, Zoran; Kocarev, Ljupco
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. By combining tools from network science and econometrics, a robust and stable relationship between a country's productive structure and its economic growth has been established. Here we report that not only goods but also services are important for predicting the rate at which countries will grow. By adopting a terminology which classifies manufactured goods and delivered services as products, we investigate the influence of services on the country's productive structure. In particular, we provide evidence that complexity indices for services are in general higher than those for goods, which is reflected in a general tendency to rank countries with developed service sector higher than countries with economy centred on manufacturing of goods. By focusing on country dynamics based on experimental data, we investigate the impact of services on the economic complexity of countries measured in the product space (consisting of both goods and services). Importantly, we show that diversification of service exports and its sophistication can provide an additional route for economic growth in both developing and developed countries.
van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A
This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.
Kyle, Kristopher; Crossley, Scott; Berger, Cynthia
This study introduces the second release of the Tool for the Automatic Analysis of Lexical Sophistication (TAALES 2.0), a freely available and easy-to-use text analysis tool. TAALES 2.0 is housed on a user's hard drive (allowing for secure data processing) and is available on most operating systems (Windows, Mac, and Linux). TAALES 2.0 adds 316 indices to the original tool. These indices are related to word frequency, word range, n-gram frequency, n-gram range, n-gram strength of association, contextual distinctiveness, word recognition norms, semantic network, and word neighbors. In this study, we validated TAALES 2.0 by investigating whether its indices could be used to model both holistic scores of lexical proficiency in free writes and word choice scores in narrative essays. The results indicated that the TAALES 2.0 indices could be used to explain 58% of the variance in lexical proficiency scores and 32% of the variance in word-choice scores. Newly added TAALES 2.0 indices, including those related to n-gram association strength, word neighborhood, and word recognition norms, featured heavily in these predictor models, suggesting that TAALES 2.0 represents a substantial upgrade.
Alexander, Gregory L; Madsen, Richard W; Miller, Erin L; Wakefield, Douglas S; Wise, Keely K; Alexander, Rachel L
To test for significant differences in information technology sophistication (ITS) in US nursing homes (NH) based on location. We administered a primary survey January 2014 to July 2015 to NH in each US state. The survey was cross-sectional and examined 3 dimensions (IT capabilities, extent of IT use, degree of IT integration) among 3 domains (resident care, clinical support, administrative activities) of ITS. ITS was broken down by NH location. Mean responses were compared across 4 NH categories (Metropolitan, Micropolitan, Small Town, and Rural) for all 9 ITS dimensions and domains. Least square means and Tukey's method were used for multiple comparisons. Methods yielded 815/1,799 surveys (45% response rate). In every health care domain (resident care, clinical support, and administrative activities) statistical differences in facility ITS occurred in larger (metropolitan or micropolitan) and smaller (small town or rural) populated areas. This study represents the most current national assessment of NH IT since 2004. Historically, NH IT has been used solely for administrative activities and much less for resident care and clinical support. However, results are encouraging as ITS in other domains appears to be greater than previously imagined. © 2016 National Rural Health Association.
Akam, Thomas; Costa, Rui; Dayan, Peter
The recently developed 'two-step' behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects' investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues.
Akam, Thomas; Costa, Rui; Dayan, Peter
The recently developed ‘two-step’ behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects’ investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues. PMID:26657806
Full Text Available The recently developed 'two-step' behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects' investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues.
Zhai, Changhai; Wang, Xiaomin; Kong, Jingchang; Li, Shuang; Xie, Lili
The development of a powerful numerical model to simulate the fracture behavior of concrete material has long been one of the dominant research areas in earthquake engineering. A reliable model should be able to adequately represent the discontinuous characteristics of cracks and simulate various failure behaviors under complicated loading conditions. In this paper, a numerical formulation, which incorporates a sophisticated rigid-plastic interface constitutive model coupling cohesion softening, contact, friction and shear dilatation into the XFEM, is proposed to describe various crack behaviors of concrete material. An effective numerical integration scheme for accurately assembling the contribution to the weak form on both sides of the discontinuity is introduced. The effectiveness of the proposed method has been assessed by simulating several well-known experimental tests. It is concluded that the numerical method can successfully capture the crack paths and accurately predict the fracture behavior of concrete structures. The influence of mode-II parameters on the mixed-mode fracture behavior is further investigated to better determine these parameters.
Full Text Available Opportunity identification is the key element of the entrepreneurial process; therefore the issue of developing this skill in students is a crucial task in contemporary European education which has recognized entrepreneurship as one of the lifelong learning key competences. The earlier opportunity identification becomes a habitual way of thinking and behavior across a broad range of contexts, the more likely that entrepreneurial disposition will steadily reside in students. In order to nurture opportunity identification in students for making them able to organize sophisticated businesses in the future, certain demands ought to be put forward as well to the teacher – the person who is to promote these qualities in their students. The paper reflects some findings of a research conducted within the frameworks of a workplace learning project for the teachers of one of Riga secondary schools (Latvia. The main goal of the project was to teach the teachers to identify hidden inner links between apparently unrelated things, phenomena and events within 10th grade study curriculum and connect them together and create new opportunities. The creation and solution of cross-disciplinary tasks were the means for achieving this goal.
Fushing, Hsieh; McAssey, Michael P; Beisner, Brianne; McCowan, Brenda
We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.
Full Text Available We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.
Benjamin L. Cook
Full Text Available Natural language processing (NLP and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12. Predictor variables included structured items (e.g., relating to sleep and well-being and responses to one unstructured question, “how do you feel today?” We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4 were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.
Cook, Benjamin L; Progovac, Ana M; Chen, Pei; Mullin, Brian; Hou, Sherry; Baca-Garcia, Enrique
Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, "how do you feel today?" We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.
Lin, Ching-Heng; Wu, Nai-Yuan; Lai, Wei-Shao; Liou, Der-Ming
Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, pgenerating entry-level interoperable clinical documents. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: firstname.lastname@example.orgFor numbered affiliation see end of article.
Bouaziz, J; Mashiach, R; Cohen, S; Kedem, A; Baron, A; Zajicek, M; Feldman, I; Seidman, D; Soriano, D
Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.
Roy, S. G.; Koons, P. O.; Gerbi, C. C.; Capps, D. K.; Tucker, G. E.; Rogers, Z. A.
Sophisticated numerical tools exist for modeling geomorphic processes and linking them to tectonic and climatic systems, but they are often seen as inaccessible for users with an exploratory level of interest. We have improved the accessibility of landscape evolution models by producing a simple graphics user interface (GUI) that takes advantage of the Channel-Hillslope Integrated Landscape Development (CHILD) model. Model access is flexible: the user can edit values for basic geomorphic, tectonic, and climate parameters, or obtain greater control by defining the spatiotemporal distributions of those parameters. Users can make educated predictions by choosing their own parametric values for the governing equations and interpreting the results immediately through model graphics. This method of modeling allows users to iteratively build their understanding through experimentation. Use of this GUI is intended for inquiry and discovery-based learning activities. We discuss a number of examples of how the GUI can be used at the upper high school, introductory university, and advanced university level. Effective teaching modules initially focus on an inquiry-based example guided by the instructor. As students become familiar with the GUI and the CHILD model, the class can shift to more student-centered exploration and experimentation. To make model interpretations more robust, digital elevation models can be imported and direct comparisons can be made between CHILD model results and natural topography. The GUI is available online through the University of Maine's Earth and Climate Sciences website, through the Community Surface Dynamics Modeling System (CSDMS) model repository, or by contacting the corresponding author.
Patitz, Matthew John
Self-assembly is the process whereby relatively simple components autonomously combine to form more complex objects. Nature exhibits self-assembly to form everything from microscopic crystals to living cells to galaxies. With a desire to both form increasingly sophisticated products and to understand the basic components of living systems, scientists have developed and studied artificial self-assembling systems. One such framework is the Tile Assembly Model introduced by Erik Winfree in 1998. In this model, simple two-dimensional square 'tiles' are designed so that they self-assemble into desired shapes. The work in this thesis consists of a series of results which build toward the future goal of designing an abstracted, high-level programming language for designing the molecular components of self-assembling systems which can perform powerful computations and form into intricate structures. The first two sets of results demonstrate self-assembling systems which perform infinite series of computations that characterize computably enumerable and decidable languages, and exhibit tools for algorithmically generating the necessary sets of tiles. In the next chapter, methods for generating tile sets which self-assemble into complicated shapes, namely a class of discrete self-similar fractal structures, are presented. Next, a software package for graphically designing tile sets, simulating their self-assembly, and debugging designed systems is discussed. Finally, a high-level programming language which abstracts much of the complexity and tedium of designing such systems, while preventing many of the common errors, is presented. The summation of this body of work presents a broad coverage of the spectrum of desired outputs from artificial self-assembling systems and a progression in the sophistication of tools used to design them. By creating a broader and deeper set of modular tools for designing self-assembling systems, we hope to increase the complexity which is
With every passing year, our experiences of the human nature have continued to teach us more about the very nature of man. Consequently, there has been the need to unlearn much of what has turned out to be prejudices and errors in our conception of man. This notwithstanding, the question “What is Man?
5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users\\' priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users\\' QoS.
Alexander, Gregory L; Pasupathy, Kalyan S; Steege, Linsey M; Strecker, E Bradley; Carley, Kathleen M
The role of nursing home (NH) information technology (IT) in quality improvement has not been clearly established, and its impacts on communication between care givers and patient outcomes in these settings deserve further attention. In this research, we describe a mixed method approach to explore communication strategies used by healthcare providers for resident skin risk in NH with high IT sophistication (ITS). Sample included NH participating in the statewide survey of ITS. We incorporated rigorous observation of 8- and 12-h shifts, and focus groups to identify how NH IT and a range of synchronous and asynchronous tools are used. Social network analysis tools and qualitative analysis were used to analyze data and identify relationships between ITS dimensions and communication interactions between care providers. Two of the nine ITS dimensions (resident care-technological and administrative activities-technological) and total ITS were significantly negatively correlated with number of unique interactions. As more processes in resident care and administrative activities are supported by technology, the lower the number of observed unique interactions. Additionally, four thematic areas emerged from staff focus groups that demonstrate how important IT is to resident care in these facilities including providing resident-centered care, teamwork and collaboration, maintaining safety and quality, and using standardized information resources. Our findings in this study confirm prior research that as technology support (resident care and administrative activities) and overall ITS increases, observed interactions between staff members decrease. Conversations during staff interviews focused on how technology facilitated resident centered care through enhanced information sharing, greater virtual collaboration between team members, and improved care delivery. These results provide evidence for improving the design and implementation of IT in long term care systems to support
most specific. For example, ’ ’’’’ the net in Figure 34 shows that: a dog is an animal, a Schnauzer is a .-. type of dog, and Bert is a Schnauzer ...specifically, is true (by default) of .,.--- the concept below it on the hierarchy. Thus, since a dog is an animal and a Schnauzer is a dog, a... Schnauzer is an animal (and Bert, because he .. ’- 63•... ,..4.,. . .-4 is a Schnauzer , is a dog, and therefore is an animal, etc). A further refinement of
McKeown, Kathleen; And Others
Presents an approach to summarization that combines information from multiple facts into a single sentence using linguistic constructions. Describes two applications: one produces summaries of basketball games, and the other contains summaries of telephone network planning activity. Both summarize input data as opposed to full text. Discusses…
The author studies the synonyms of 'skinny' and 'fatty' from the cognitive linguistic perspective. The quantum of the analysed lexical items is subdivided into the following type-groups: zoosemy (animal metaphor), foodsemy (food metaphor), plantosemy (plant metaphor), metonymy, reification, eponymy, onomatopoeia, rhyming slang and varia.
Willems, R.M.; Frank, S.L.; Nijhof, A.D.; Hagoort, P.; Bosch, A.P.J. van den
The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as
adaptable organisms over those which contain, built-in optimized features. Compare the extinct dinosaur , unable to adapt to new situations, with two of...natural language understandirq for KRL [Bobrow & Winograd] and OWL [Szolovits, et alD. For this reason , his language is ofL.:n inadequate for any...for no particular reason , switch the organ into its Oboe state. That is, the sequence which triggers a change to the organ is a Punction of the organ’s
Mezgec, Simon; Eftimov, Tome; Bucher, Tamara; Koroušić Seljak, Barbara
The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis. The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors. The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %. The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.
Gálvez, Jorge A; Pappas, Janine M; Ahumada, Luis; Martin, John N; Simpao, Allan F; Rehman, Mohamed A; Witmer, Char
Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool's sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.
Functionalized poly(p-xylylenes) or so-called reactive polymers can be synthesized via chemical vapor deposition (CVD) polymerization. The resulting ultra-thin coatings are pinhole-free and can be conformally deposited to a wide range of substrates and materials. More importantly, the equipped functional groups can served as anchoring sites for tailoring the surface properties, making these reactive coatings a robust platform that can deal with sophisticated challenges faced in biointerfaces. In this work presented herein, surface coatings presenting various functional groups were prepared by CVD process. Such surfaces include aldehyde-functionalized coating to precisely immobilize saccharide molecules onto well-defined areas and alkyne-functionalized coating to click azide-modified molecules via Huisgen 1,3-dipolar cycloaddition reaction. Moreover, CVD copolymerization has been conducted to prepare multifunctional coatings and their specific functions were demonstrated by the immobilization of biotin and NHS-ester molecules. By using a photodefinable coating, polyethylene oxides were immobilized onto a wide range of substrates through photo-immobilization. Spatially controlled protein resistant properties were characterized by selective adsorption of fibrinogen and bovine serum albumin as model systems. Alternatively, surface initiator coatings were used for polymer graftings of polyethylene glycol) methyl ether methacrylate, and the resultant protein- and cell- resistant properties were characterized by adsorption of kinesin motor proteins, fibrinogen, and murine fibroblasts (NIH3T3). Accessibility of reactive coatings within confined microgeometries was systematically studied, and the preparation of homogeneous polymer thin films within the inner surface of microchannels was demonstrated. Moreover, these advanced coatings were applied to develop a dry adhesion process for microfluidic devices. This process provides (i) excellent bonding strength, (ii) extended
Cox, Robert Sidney
As protein engineering becomes more sophisticated, practitioners increasingly need to share diagrams for communicating protein designs. To this end, we present a draft visual language, Protein Language, that describes the high-level architecture of an engineered protein with easy-to draw glyphs, intended to be compatible with other biological diagram languages such as SBOL Visual and SBGN. Protein Language consists of glyphs for representing important features (e.g., globular domains, recognition and localization sequences, sites of covalent modification, cleavage and catalysis), rules for composing these glyphs to represent complex architectures, and rules constraining the scaling and styling of diagrams. To support Protein Language we have implemented an extensible web-based software diagram tool, Protein Designer, that uses Protein Language in a
Cox, Robert Sidney; McLaughlin, James Alastair; Grunberg, Raik; Beal, Jacob; Wipat, Anil; Sauro, Herbert M.
As protein engineering becomes more sophisticated, practitioners increasingly need to share diagrams for communicating protein designs. To this end, we present a draft visual language, Protein Language, that describes the high-level architecture of an engineered protein with easy-to draw glyphs, intended to be compatible with other biological diagram languages such as SBOL Visual and SBGN. Protein Language consists of glyphs for representing important features (e.g., globular domains, recognition and localization sequences, sites of covalent modification, cleavage and catalysis), rules for composing these glyphs to represent complex architectures, and rules constraining the scaling and styling of diagrams. To support Protein Language we have implemented an extensible web-based software diagram tool, Protein Designer, that uses Protein Language in a