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
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Maße
Breite: 242 mm
Dicke: 38 mm
Gewicht
ISBN-13
978-0-13-095069-7 (9780130950697)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
