Speech and Language Processing

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2008-05-16
  • Publisher: Pearson

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An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.

Author Biography

Dan Jurafsky is an associate professor in the Department of Linguistics, and by courtesy in Department of Computer Science, at Stanford University. Previously, he was on the faculty of the University of Colorado, Boulder, in the Linguistics and Computer Science departments and the Institute of Cognitive Science. He was born in Yonkers, New York, and received a B.A. in Linguistics in 1983 and a Ph.D. in Computer Science in 1992, both from the University of California at Berkeley. He received the National Science Foundation CAREER award in 1998 and the MacArthur Fellowship in 2002. He has published over 90 papers on a wide range of topics in speech and language processing.


James H. Martin is a professor in the Department of Computer Science and in the Department of Linguistics, and a fellow in the Institute of Cognitive Science at the University of Colorado at Boulder. He was born in New York City, received a B.S. in Comoputer Science from Columbia University in 1981 and a Ph.D. in Computer Science from the University of California at Berkeley in  1988. He has authored over 70 publications in computer science including the book A Computational Model of Metaphor Interpretation.

Table of Contents

About the Authors
Knowledge in Speech and Language Processing
Models and Algorithms
Language, Thought, and Understanding
The State of the Art
Some Brief History
Foundational Insights: 1940s and 1950s
The Two Camps: 1957 1970
Four Paradigms: 1970 1983
Empiricism and Finite State Models Redux: 1983 1993
The Field Comes Together: 1994 1999
The Rise of Machine Learning: 2000 2008
On Multiple Discoveries
A Final Brief Note on Psychology
SummaryBibliographical and Historical Notes
Words2 Regular Expressions and Automata
Regular Expressions
Basic Regular Expression Patterns
Disjunction, Grouping, and Precedence
A Simple Example
A More Complex Example
Advanced Operators
Regular Expression Substitution, Memory, and ELIZA
Finite-State Automata
Using an FSA to Recognize Sheeptalk
Formal Languages
Another Example
Non-Deterministic FSAs
Using an NFSA to Accept Strings
Recognition as Search
Relating Deterministic and Non-Deterministic Automata
Regular Languages and FSAs
SummaryBibliographical and Historical NotesExercises3 Words and Transducers
Survey of (Mostly) English Morphology
Inflectional Morphology
Derivational Morphology
Non-Concatenative Morphology
Finite-State Morphological Parsing
Construction of a Finite-State Lexicon
Finite-State Transducers
Sequential Transducers and Determinism
FSTs for Morphological Parsing
Transducers and Orthographic Rules
The COmbination of an FST Lexicon and Rules
Lexicon-Free FSTs: The Porter Stemmer
Word and Sentence Tokenization
Segmentation in Chinese
Detection and Correction of Spelling Errors
Minimum Edit Distance
Human Morphological Processing
SummaryBibliographical and Historical NotesExercises4 N-grams
Word Counting in Corpora
Simple (Unsmoothed) N-grams
Training and Test Sets
N-gram Sensitivity to the Training Corpus
Unknown Words: Open Versus Closed Vocabulary Tasks
Evaluating N-grams: Perplexity
Laplace Smoothing
Good-Turing Discounting
Some Advanced Issues in Good-Turing Estimation
Advanced: Details of Computing Katz Backoff a and P
Practical Issues: Toolkits and Data Formats
Advanced Issues in Language Modeling
Advanced Smoothing Methods: Kneser-Ney Smoothing
Class-Based N-grams
Language Model Adaptation and Web Use
Using Longer Distance Information: A Brief Summary
Advanced: Information Theory Background
Cross-Entropy for Comparing Models
Advanced: The Entropy of English and Entropy Rate Constancy
SummaryBibliographical and Historical NotesExercises5 Part-of-Speech Tagging
(Mostly) English Word Classes
Tagsets for English
Part-of-Speech Tagging
Rule-Based Part-of-Speech Tagging
HMM Part-of-Speech Tagging
Computing the Most-Likely Tag Sequence: An Example
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