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

By: Jurafsky, DanielContributor(s): Martin, James HMaterial type: TextTextPublication details: Delhi : Pearson Education, 2000Description: xxvi, 934 pISBN: 0130950696 ; 9780130950697; 8178085941; 9788178085944 Subject(s): Automatic speech recognitionDDC classification: 410.285
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.
Summary: This work takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Reference Books Reference Books Main Library
Reference 		Reference 410.285 JUR (Browse shelf(Opens below)) Available 009504
Total holds: 0

Included Index.

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.

This work takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.

There are no comments on this title.

to post a comment.

© University of Vavuniya

---