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Probabilistic Approaches to Linguistic Theory
CSLI, 2022 Paper: 978-1-68400-079-1 | eISBN: 978-1-68400-080-7 Library of Congress Classification P98 Dewey Decimal Classification 410.285
ABOUT THIS BOOK | AUTHOR BIOGRAPHY | TOC
ABOUT THIS BOOK
A textbook exploring predictive modes of linguistic development and analysis. During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science. See other books on: Computational linguistics | Lappin, Shalom | Linguistic Theory | Logic | Natural language processing (Computer science) See other titles from CSLI |
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Nearby on shelf for Philology. Linguistics / Computational linguistics. Natural language processing:
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Probabilistic Approaches to Linguistic Theory
ABOUT THIS BOOK | AUTHOR BIOGRAPHY | TOC
CSLI, 2022
Paper: 978-1-68400-079-1
eISBN: 978-1-68400-080-7
Paper: 978-1-68400-079-1
eISBN: 978-1-68400-080-7
A textbook exploring predictive modes of linguistic development and analysis.
During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science.
During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science.
ABOUT THIS BOOK | AUTHOR BIOGRAPHY | TOC
