Speech and Language Processing
by Jurafsky, Daniel; Martin, James H.Edition: 2nd
Format: Hardcover
Pub. Date: 2008-05-16
Publisher(s): Pearson
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Summary
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
| Foreword | |
| Preface | |
| About the Authors | |
| Introduction | |
| Knowledge in Speech and Language Processing | |
| Ambiguity | |
| 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 | |
| Cliticization | |
| Non-Concatenative Morphology | |
| Agreement | |
| 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 | |
| Smoothing | |
| Laplace Smoothing | |
| Good-Turing Discounting | |
| Some Advanced Issues in Good-Turing Estimation | |
| Interpolation | |
| Backoff | |
| 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 | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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