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This handbook offers a thorough treatment of the science of linguistic annotation. Leaders in the field guide the reader through the process of modeling, creating an annotation language, building a corpus and evaluating it for correctness. Essential reading for both computer scientists and linguistic researchers.Linguistic annotation is an increasingly important activity in the field of computational linguistics because of its critical role in the development of language models for natural language processing applications. Part one of this book covers all phases of the linguistic annotation process, from annotation scheme design and choice of representation format through both the manual and automatic annotation process, evaluation, and iterative improvement of annotation accuracy. The second part of the book includes case studies of annotation projects across the spectrum of linguistic annotation types, including morpho-syntactic tagging, syntactic analyses, a range of semantic analyses (semantic roles, named entities, sentiment and opinion), time and event and spatial analyses, and discourse level analyses including discourse structure, co-reference, etc. Each case study addresses the various phases and processes discussed in the chapters of part one.
Now available in paperback for the first time since its original publication, the material in this book provides a broad, accessible guide to semantic typology, crosslinguistic semantics and diachronic semantics. Coming from a world-leading team of authors, the book also deals with the concept of meaning in psycholinguistics and neurolinguistics, and the understanding of semantics in computer science. It is packed with highly cited, expert guidance on the key topics in the field, making it a bookshelf essential for linguists, cognitive scientists, philosophers, and computer scientists working on natural language.
The majority of information on the Internet is expressed in written text. Understanding and extracting this information is crucial to building intelligent systems that can organize this knowledge, but most algorithms focus on learning atomic facts and relations. For instance, we can reliably extract facts like "Stanford is a University" and "Professors teach Science" by observing redundant word patterns across a corpus. However, these facts do not capture richer knowledge like the way detonating a bomb is related to destroying a building, or that the perpetrator who was convicted must have been arrested. A structured model of these events and entities is needed to understand language across many genres, including news, blogs, and even social media. This dissertation describes a new approach to knowledge acquisition and extraction that learns rich structures of events (e.g., plant, detonate, destroy) and participants (e.g., suspect, target, victim) over a large corpus of news articles, beginning from scratch and without human involvement. As opposed to early event models in Natural Language Processing (NLP) such as scripts and frames, modern statistical approaches and advances in NLP now enable new representations and large-scale learning over many domains. This dissertation begins by describing a new model of events and entities called Narrative Event Schemas. A Narrative Event Schema is a collection of events that occur together in the real world, linked by the typical entities involved. I describe the representation itself, followed by a statistical learning algorithm that observes chains of entities repeatedly connecting the same sets of events within documents. The learning process extracts thousands of verbs within schemas from 14 years of newspaper data. I present novel contributions in the field of temporal ordering to build classifiers that order the events and infer likely schema orderings. I then present several new evaluations for the extracted knowledge. Finally, I apply Narrative Event Schemas to the field of Information Extraction, learning templates of events with sets of semantic roles. Most Information Extraction approaches assume foreknowledge of the domain's templates, but I instead start from scratch and learn schemas as templates, and then extract the entities from text as in a standard extraction task. My algorithm is the first to learn templates without human guidance, and its results approach those of supervised algorithms.
The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.
The first edition of ELL (1993, Ron Asher, Editor) was hailed as "the field's standard reference work for a generation". Now the all-new second edition matches ELL's comprehensiveness and high quality, expanded for a new generation, while being the first encyclopedia to really exploit the multimedia potential of linguistics. * The most authoritative, up-to-date, comprehensive, and international reference source in its field * An entirely new work, with new editors, new authors, new topics and newly commissioned articles with a handful of classic articles * The first Encyclopedia to exploit the multimedia potential of linguistics through the online edition * Ground-breaking and International in scope and approach * Alphabetically arranged with extensive cross-referencing * Available in print and online, priced separately. The online version will include updates as subjects develop ELL2 includes: * c. 7,500,000 words * c. 11,000 pages * c. 3,000 articles * c. 1,500 figures: 130 halftones and 150 colour * Supplementary audio, video and text files online * c. 3,500 glossary definitions * c. 39,000 references * Extensive list of commonly used abbreviations * List of languages of the world (including information on no. of speakers, language family, etc.) * Approximately 700 biographical entries (now includes contemporary linguists) * 200 language maps in print and online Also available online via ScienceDirect – featuring extensive browsing, searching, and internal cross-referencing between articles in the work, plus dynamic linking to journal articles and abstract databases, making navigation flexible and easy. For more information, pricing options and availability visit www.info.sciencedirect.com. The first Encyclopedia to exploit the multimedia potential of linguistics Ground-breaking in scope - wider than any predecessor An invaluable resource for researchers, academics, students and professionals in the fields of: linguistics, anthropology, education, psychology, language acquisition, language pathology, cognitive science, sociology, the law, the media, medicine & computer science. The most authoritative, up-to-date, comprehensive, and international reference source in its field
Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the “factual” aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented. The models explored in this book include the following: distinguishing attitudes from simple factual assertions; distinguishing between the author’s reports from reports of other people’s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, including indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups about consumer products, political issues, etc. ; brand and reputation management; discovering and predicting consumer and voting trends; analyzing client discourse in therapy and counseling; determining relations between scientific texts by finding reasons for citations; generating more appropriate texts and making agents more believable; and creating writers’ aids. The studies reported here are carried out on different languages such as English, French, Japanese, and Portuguese. Difficult challenges remain, however. It can be argued that analyzing attitude and affect in text is an “NLP”-complete problem.
This book presents a theory of consciousness which is unique and sustainable in nature, based on physiological and cognitive-linguistic principles controlled by a number of socio-psycho-economic factors. In order to anchor this theory, which draws upon various disciplines, the author presents a number of different theories, all of which have been abundantly studied by scientists from both a theoretical and experimental standpoint, including models of social organization, ego theories, theories of the motivational system in psychology, theories of the motivational system in neurosciences, language modeling and computational modeling of motivation. The theory presented in this book is based on the hypothesis that an individual’s main activities are developed by self-motivation, managed as an informational need. This is described in chapters covering self-motivation on a day-to-day basis, the notion of need, the hypothesis and control of cognitive self-motivation and a model of self-motivation which associates language and physiology. The subject of knowledge extraction is also covered, including the impact of self-motivation on written information, non-transversal and transversal text-mining techniques and the fields of interest of text mining. Contents: 1. Consciousness: an Ancient and Current Topic of Study. 2. Self-motivation on a Daily Basis. 3. The Notion of Need. 4. The Models of Social Organization. 5. Self Theories. 6. Theories of Motivation in Psychology. 7. Theories of Motivation in Neurosciences. 8. Language Modeling. 9. Computational Modeling of Motivation. 10. Hypothesis and Control of Cognitive Self-Motivation. 11. A Model of Self-Motivation which Associates Language and Physiology. 12. Impact of Self-Motivation on Written Information. 13. Non-Transversal Text Mining Techniques. 14. Transversal Text Mining Techniques. 15. Fields of Interest for Text Mining. About the Authors Nicolas Turenne is a researcher at INRA in the Science and Society team at the University of Paris-Est Marne la Vallée in France. He specializes in knowledge extraction from texts with theoretical research into relational and stochastic models. His research topics also concern the sociology of uses, food and environmental sciences, and bioinformatics.
The Cambridge Handbook of Morphology describes the diversity of morphological phenomena in the world's languages, surveying the methodologies by which these phenomena are investigated and the theoretical interpretations that have been proposed to explain them. The Handbook provides morphologists with a comprehensive account of the interlocking issues and hypotheses that drive research in morphology; for linguists generally, it presents current thought on the interface of morphology with other grammatical components and on the significance of morphology for understanding language change and the psychology of language; for students of linguistics, it is a guide to the present-day landscape of morphological science and to the advances that have brought it to its current state; and for readers in other fields (psychology, philosophy, computer science, and others), it reveals just how much we know about systematic relations of form to content in a language's words - and how much we have yet to learn.