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Speech and Language Processing : An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by James H. Martin and Daniel Jurafsky (2000, Hardcover)

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Informazioni su questo prodotto

Product Identifiers

PublisherPearson Education

ISBN-100130950696

ISBN-139780130950697

eBay Product ID (ePID)1101471

Product Key Features

Number of Pages934 Pages

LanguageEnglish

Publication NameSpeech and Language Processing : An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Publication Year2000

SubjectComputer Vision & Pattern Recognition, Linguistics / General

TypeTextbook

Subject AreaComputers, Language Arts & Disciplines

AuthorJames H. Martin, Daniel Jurafsky

FormatHardcover

Dimensions

Item Height1.5 in

Item Weight52 Oz

Item Length9.5 in

Item Width7.3 in

Additional Product Features

Intended AudienceCollege Audience

LCCN99-087845

Dewey Edition22

IllustratedYes

Dewey Decimal410.2/85

Table Of Content1. Introduction. I. WORDS. 2. Regular Expressions and Automata. 3. Morphology and Finite-State Transducers. 4. Computational Phonology and Text-to-Speech. 5. Probabilistic Models of Pronunciation and Spelling. 6. N-grams. 7. HMMs and Speech Recognition. II. SYNTAX. 8. Word Classes and Part-of-Speech Tagging. 9. Context-Free Grammars for English. 10. Parsing with Context-Free Grammars. 11. Features and Unification. 12. Lexicalized and Probabilistsic Parsing. 13. Language and Complexity. III. SEMANTICS. 14. Representing Meaning. 15. Semantic Analysis. 16. Lexical Semantics. 17. Word Sense Disambiguation and Information Retrieval. IV. PRAGMATICS. 18. Discourse. 19. Dialogue and Conversational Agents. 20. Natural Language Generation. 21. Machine Translation. APPENDICES. A. Regular Expression Operators. B. The Porter Stemming Algorithm. C. C5 and C7 tagsets. D. Training HMMs: The Forward-Backward Algorithm. Bibliography. Index.

SynopsisThis book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing., For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corporations. Author Website with Resources: http://www.cs.colorado.edu/~martin/slp.html

LC Classification NumberP98.J87 2000