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Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction. This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.
This extended collection of papers is the result of putting recent ideas on quantification to work on a wide variety of languages. A central perspective of many of the papers follows the recognition of two broad types of quantificational strategies, one associated with nominal structures and determiners, the other with adverbial and other non-nominal expression (`D-quantifiers' and `A-quantifiers'). The papers demonstrate both the unity and the variety of natural language quantificational forms and meanings. Many of the papers also shed new light on questions of language typology and syntactic and morphological variation. The languages discussed include English, Dutch, Italian, American Sign Language, Hindi, and a number of languages of Australia, Greenland, and the Americas. These comparative studies provide initial data for a typology of quantificational structures in natural languages, with important implications for the study of universal grammar. The book consists of research papers aimed at linguists, philosophers, and psychologists interested in semantics and linguistic form. An introduction presents a sketch of the background of this research and some of the central issues discussed, with pointers toward the included papers.
Proceedings of the 17th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Kaiserslautern, March 3 - 5, 1993
Quantification is a topic which brings together linguistics, logic, and philosophy. Quantifiers are the essential tools with which, in language or logic, we refer to quantity of things or amount of stuff. In English they include such expressions as no, some, all, both, and many. Peters and Westerstahl present the definitive interdisciplinary exploration of how they work - their syntax, semantics, and inferential role.Quantifiers in Language and Logic is intended for everyone with a scholarly interest in the exact treatment of meaning. It presents a broad view of the semantics and logic of quantifier expressions in natural languages and, to a slightly lesser extent, in logical languages. The authors progress carefully from a fairly elementary level to considerable depth over the course of sixteen chapters; their book will be invaluable to a broad spectrum of readers, from those with a basicknowledge of linguistic semantics and of first-order logic to those with advanced knowledge of semantics, logic, philosophy of language, and knowledge representation in artificial intelligence.
This volume contains versions of invited addresses and communications for the First Chilean Symposium of Mathematics, revealing the results of the mathematical advances in areas such as stochastic analysis, solutions of differential equations, and differential synthetic geometry and probability.
This book constitutes the refereed proceedings of the 4th International Workshop on Controlled Natural Language, CNL 2014, held in Galway, Ireland, in August 2014. The 17 full papers and one invited paper presented were carefully reviewed and selected from 26 submissions. The topics include simplified language, plain language, formalized language, processable language, fragments of language, phraseologies, conceptual authoring, language generation, and guided natural language interfaces.
Research in Natural Language Processing (NLP) has rapidly advanced in recent years, resulting in exciting algorithms for sophisticated processing of text and speech in various languages. Much of this work focuses on English; in this book we address another group of interesting and challenging languages for NLP research: the Semitic languages. The Semitic group of languages includes Arabic (206 million native speakers), Amharic (27 million), Hebrew (7 million), Tigrinya (6.7 million), Syriac (1 million) and Maltese (419 thousand). Semitic languages exhibit unique morphological processes, challenging syntactic constructions and various other phenomena that are less prevalent in other natural languages. These challenges call for unique solutions, many of which are described in this book. The 13 chapters presented in this book bring together leading scientists from several universities and research institutes worldwide. While this book devotes some attention to cutting-edge algorithms and techniques, its primary purpose is a thorough explication of best practices in the field. Furthermore, every chapter describes how the techniques discussed apply to Semitic languages. The book covers both statistical approaches to NLP, which are dominant across various applications nowadays and the more traditional, rule-based approaches, that were proven useful for several other application domains. We hope that this book will provide a "one-stop-shop'' for all the requisite background and practical advice when building NLP applications for Semitic languages.
These proceedings of the Fourth International Conference on Flexible Query Answering Systems covers the whole array of fields related to users posing flexible queries and (electronic) systems producing answers. The FQAS 2000 Conference has been the premier conference focusing on one of the key issues the information society is facing, namely that of providing easy, flexible, intuitive access to information to everybody. In targeting this issue, the conference draws on several research areas such as databases, querying, information retrieval, knowledge representation, soft computing, cyberspace, multimedia systems, human-computer interaction. This volume provides a unique opportunity for researchers, developers and practitioners to explore new ideas and approaches in a multidisciplinary forum.
This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.