Algebraic Structures in Natural Language
Lappin, Shalom (Editor)/ Bernardy, Jean-philippe (Editor)
ISBN 10: 1032071044 ISBN 13: 9781032071046
Editorial: CRC Pr I Llc, 2024
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Revaluation Books, Exeter, Reino Unido
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Descripción
Descripción:
308 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand. N° de ref. del artículo __1032071044
Sinopsis:
Algebraic Structures in Natural Language by bringing together leading researchers from computational and mathematical linguistics, psychology and behavioural science, addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties.
Acerca del autor:
Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg, Professor of Natural Language Processing at Queen Mary University of London and Emeritus Professor of Computational Linguistics at King’s College London. His research focuses on the application of machine learning and probabilistic models to the representation and the acquisition of linguistic knowledge.
Jean-Philippe Bernardy is a researcher at the University of Gothenburg. His main research interest is in interpretable linguistic models, in particular, those built from first principles of algebra, probability and geometry.
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Detalles bibliográficos
Título: Algebraic Structures in Natural Language
Editorial: CRC Pr I Llc
Año de publicación: 2024
Encuadernación: Paperback
Condición: Brand New
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Paperback. Condición: new. Paperback. 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. Algebraic Structures in Natural Language by bringing together leading researchers from computational and mathematical linguistics, psychology and behavioural science, addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032071046
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Paperback. Condición: new. Paperback. 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. Algebraic Structures in Natural Language by bringing together leading researchers from computational and mathematical linguistics, psychology and behavioural science, addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781032071046
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