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Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition

Author: Dan Jurafsky; James H Martin
Publisher: Upper Saddle River, NJ ; London : Pearson Education, 2009.
Series: Prentice Hall series in artificial intelligence.
Edition/Format:   Print book : English : 2nd edView all editions and formats
Summary:
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  Read more...
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Details

Document Type: Book
All Authors / Contributors: Dan Jurafsky; James H Martin
ISBN: 9780135041963 0135041961
OCLC Number: 962364784
Notes: "Pearson international edition"--Cover.
"Pearson Education International."
Description: 1024 pages : illustrations ; 24 cm
Contents: 1. Introduction --
1. I Words --
2. Regular Expressions and Automata --
3. Words and Transducers --
4. N-grams --
5. Part-of-Speech Tagging --
6. Hidden Markov and Maximum Entropy Models --
II Speech --
7. Phonetics --
8. Speech Synthesis --
9. Automatic Speech Recognition --
10. Speech Recognition: Advanced Topics --
11. Computational Phonology --
III. Syntax --
12. Formal Grammars of English --
13. Parsing with Context-Free Grammars --
14. Statistical Parsing --
15. Features and Uni-cation --
16. Language and Complexity --
IV. Semantics and pragmatics --
17. Representing Meaning --
18. Computational Semantics --
19. Lexical Semantics --
20. Computational Lexical Semantics --
21. Computational Discourse --
V. Applications --
22. Information Extraction --
23. Question Answering and Summarization --
24. Dialogue and Conversational Agents --
25. Machine Translation.
Series Title: Prentice Hall series in artificial intelligence.
Other Titles: Introduction to natural language processing, computational linguistics, and speech recognition
Responsibility: Daniel Jurafsky, James H. Martin.

Abstract:

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.

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Martin<\/span>\n\u00A0\u00A0\u00A0\nschema:creator<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/796420461#Person\/jurafsky_dan_1962<\/a>> ; # Dan Jurafsky<\/span>\n\u00A0\u00A0\u00A0\nschema:datePublished<\/a> \"2009<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"1. Introduction -- 1. I Words -- 2. Regular Expressions and Automata -- 3. Words and Transducers -- 4. N-grams -- 5. Part-of-Speech Tagging -- 6. Hidden Markov and Maximum Entropy Models -- II Speech -- 7. Phonetics -- 8. Speech Synthesis -- 9. Automatic Speech Recognition -- 10. Speech Recognition: Advanced Topics -- 11. Computational Phonology -- III. Syntax -- 12. Formal Grammars of English -- 13. Parsing with Context-Free Grammars -- 14. Statistical Parsing -- 15. Features and Uni-cation -- 16. Language and Complexity -- IV. Semantics and pragmatics -- 17. Representing Meaning -- 18. Computational Semantics -- 19. Lexical Semantics -- 20. Computational Lexical Semantics -- 21. 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