Speech and Language Processing : An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition / Edition 1

Speech and Language Processing : An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition / Edition 1

by Daniel Jurafsky, James H. Martin, James H. Martin, Jame H. Martin
     
 

ISBN-10: 0130950696

ISBN-13: 9780130950697

Pub. Date: 01/18/2000

Publisher: Prentice Hall

This 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,

Overview

This 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.

Product Details

ISBN-13:
9780130950697
Publisher:
Prentice Hall
Publication date:
01/18/2000
Series:
Practical Resources for the Mental Health Series
Edition description:
Older Edition
Pages:
934
Product dimensions:
6.98(w) x 9.16(h) x 1.88(d)

Table of Contents



 1. 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.

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