[Book Cover]

Speech and Language Processing, 1/e

Daniel Jurafsky
James H. Martin, both of University of Colorado, Boulder

Coming December, 1999 by Prentice Hall Engineering/Science/Mathematics

Copyright 2000, 600 pp.
Cloth
ISBN 0-13-095069-6


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Summary

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 corpora.

Features


Each chapter is built around one or more worked examples demonstrating the main idea of the chapter.

  • Uses worked examples to illustrate the relative strengths and weaknesses of various approaches.
Methodology boxes—Included in each chapter.
  • Introduces important methodological tools such as evaluation, wizard of oz techniques, etc.
Problem sets—Included in each chapter.
Integration of speech and text processing—Merges speech processing and natural language processing fields.
Empiricist/statistical/machine learning approaches to language processing—Covers all of the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.
Includes modern rigorous evaluation metrics.
Unified and comprehensive coverage of the field—Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language.
  • Shows students how the same algorithm can be used for speech recognition and word-sense disambiguation.
Emphasis on Web and other practical applications.
  • Gives students an understanding of how language-related algorithms can be applied to important real-world problems.
Emphasis on scientific evaluation—Offers a description of how systems are evaluated with each problem domain.
Description of widely available language processing resources.


Table of Contents
I. INTRODUCTION.
    1. Introduction.

II. WORDS.
    2. Regular Expressions and Automata.
    3. Morphology and Finite-State Transducers.
    4. Computational Phonology and Pronunciation Modeling.
    5. Probabilistic Models of Pronunciation and Spelling.
III. SYNTAX.
    6. N-gram Models of Syntax.
    7. HMMs and Speech Recognition.
    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. Language and Complexity.
IV. SEMANTICS.
    13. Representing Meaning.
    14. Syntax-Driven Semantic Analysis.
    15. Lexical Semantics.
V. PRAGMATICS.
    16. Discourse and Dialog.
    17. Generation.
    18. Machine Translation.
    Bibliography.

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