Speech and Language Processing (2nd Edition) [Hardcover]

Daniel Jurafsky (Author), James H. Martin (Author)

Book Description

Publication Date: May 26, 2008 | ISBN-10: 0131873210 | ISBN-13: 978-0131873216 | Edition: 2

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.

Editorial Reviews

Review

... ideal for ... linguists who want to learn more about computational modeling and techniques in language processing; computer scientists building language applications who want to learn more about the linguistic underpinnings of the field; speech technologists who want to learn more about language understanding, semantics and discourse; and all those wanting to learn more about speech processing. For instructors ... this book is a dream. It covers virtually every aspect of NLP... What's truly astounding is that the book covers such a broad range of topics, while giving the reader the depth to understand and make use of the concepts, algorithms and techniques that are presented... ideal as a course textbook for advanced undergraduates, as well as graduate students and researchers in the field. -- Johanna Moore, University of Edinburgh

Speech and Language Processing is a comprehensive, reader-friendly, and up-to-date guide to computational linguistics, covering both statistical and symbolic methods and their application. It will appeal both to senior undergraduate students, who will find it neither too technical nor too simplistic, and to researchers, who will find it to be a helpful guide to the newly established techniques of a rapidly growing research field. -- Graeme Hirst, University of Toronto

This book is an absolute necessity for instructors at all levels, as well as an indispensable reference for researchers. Introducing NLP, computational linguistics, and speech recognition comprehensively in a single book is an ambitious enterprise. The authors have managed it admirably, paying careful attention to traditional foundations, relating recent developments and trends to those foundations, and tying it all together with insight and humor. Remarkable. -- Philip Resnik, University of Maryland

This is quite simply the most complete introduction to natural language and speech technology ever written. Virtually every topic in the field is covered, in a prose style that is both clear and engaging. The discussion is linguistically informed, and strikes a nice balance between theoretical computational models, and practical applications. It is an extremely impressive achievement. -- Richard Sproat, AT&T Labs -- Research --This text refers to an out of print or unavailable edition of this title.

From the Inside Flap

Preface

This is an exciting time to be working in speech and language processing. Historically distinct fields (natural language processing, speech recognition, computational linguistics, computational psycholinguistics) have begun to merge. The commercial availability of speech recognition and the need for Web-based language techniques have provided an important impetus for development of real systems. The availability of very large on-line corpora has enabled statistical models of language at every level, from phonetics to discourse. We have tried to draw on this emerging state of the art in the design of this pedagogical and reference work:

Coverage
In attempting to describe a unified vision of speech and language processing, we cover areas that traditionally are taught in different courses in different departments: speech recognition in electrical engineering; parsing, semantic interpretation, and pragmatics in natural language processing courses in computer science departments; and computational morphology and phonology in computational linguistics courses in linguistics departments. The book introduces the fundamental algorithms of each of these fields, whether originally proposed for spoken or written language, whether logical or statistical in origin, and attempts to tie together the descriptions of algorithms from different domains. We have also included coverage of applications like spelling-checking and information retrieval and extraction as well as areas like cognitive modeling. A potential problem with this broad-coverage approach is that it required us to include introductory material for each field; thus linguists may want to skip our description of articulatory phonetics, computer scientists may want to skip such sections as regular expressions, and electrical engineers skip the sections on signal processing. Of course, even in a book this long, we didn't have room for everything. Thus this book should not be considered a substitute for important relevant courses in linguistics, automata and formal language theory, or, especially, statistics and information theory. Emphasis on Practical Applications
It is important to show how language-related algorithms and techniques (from HMMs to unification, from the lambda calculus to transformation-based learning) can be applied to important real-world problems: spelling checking, text document search, speech recognition, Web-page processing, part-of-speech tagging, machine translation, and spoken-language dialogue agents. We have attempted to do this by integrating the description of language processing applications into each chapter. The advantage of this approach is that as the relevant linguistic knowledge is introduced, the student has the background to understand and model a particular domain. Emphasis on Scientific Evaluation
The recent prevalence of statistical algorithms in language processing and the growth of organized evaluations of speech and language processing systems has led to a new emphasis on evaluation. We have, therefore, tried to accompany most of our problem domains with a Methodology Box describing how systems are evaluated (e.g., including such concepts as training and test sets, cross-validation, and information-theoretic evaluation metrics like perplexity). Description of widely available language processing resources
Modern speech and language processing is heavily based on common resources: raw speech and text corpora, annotated corpora and treebanks, standard tagsets for labeling pronunciation, part-of-speech, parses, word-sense, and dialogue-level phenomena. We have tried to introduce many of these important resources throughout the book (e.g., the Brown, Switchboard, callhome, ATIS, TREC, MUC, and BNC corpora) and provide complete listings of many useful tagsets and coding schemes (such as the Penn Treebank, CLAWS C5 and C7, and the ARPAbet) but some inevitably got left out. Furthermore, rather than include references to URLs for many resources directly in the textbook, we have placed them on the book's Web site, where they can more readily updated.

The book is primarily intended for use in a graduate or advanced undergraduate course or sequence. Because of its comprehensive coverage and the large number of algorithms, the book is also useful as a reference for students and professionals in any of the areas of speech and language processing. Overview of the Book

The book is divided into four parts in addition to an introduction and end matter. Part I, "Words", introduces concepts related to the processing of words: phonetics, phonology, morphology, and algorithms used to process them: finite automata, finite transducers, weighted transducers, N-grams, and Hidden Markov Models. Part II, "Syntax", introduces parts-of-speech and phrase structure grammars for English and gives essential algorithms for processing word classes and structured relationships among words: part-of-speech taggers based on HMMs and transformation-based learning, the CYK and Earley algorithms for parsing, unification and typed feature structures, lexicalized and probabilistic parsing, and analytical tools like the Chomsky hierarchy and the pumping lemma. Part III, "Semantics", introduces first order predicate calculus and other ways of representing meaning, several approaches to compositional semantic analysis, along with applications to information retrieval, information extraction, speech understanding, and machine translation. Part IV, "Pragmatics", covers reference resolution and discourse structure and coherence, spoken dialogue phenomena like dialogue and speech act modeling, dialogue structure and coherence, and dialogue managers, as well as a comprehensive treatment of natural language generation and of machine translation. Using this Book

The book provides enough material to be used for a full-year sequence in speech and language processing. It is also designed so that it can be used for a number of different useful one-term courses:

NLP
1 quarter NLP
1 semester Speech + NLP
1 semester Comp. Linguistics
1 quarter

1. Intro 1. Intro 1. Intro1. Intro

2. Regex, FSA 2. Regex, FSA 2. Regex, FSA2. Regex, FSA

8. POS tagging 3. Morph., FST 3. Morph., FST3. Morph., FST

9. CFGs 6. N-grams 4. Comp. Phonol.4. Comp. Phonol.

10. Parsing 8. POS tagging 5. Prob. Pronun.10. Parsing

11. Unification 9. CFGs 6. N-grams11. Unification

14. Semantics 10. Parsing 7. HMMs & ASR13. Complexity

15. Sem. Analysis 11. Unification 8. POS tagging16. Lex. Semantics

18. Discourse 12. Prob. Parsing 9. CFGs18. Discourse

20. Generation 14. Semantics 10. Parsing19. Dialogue

15. Sem. Analysis 12. Prob. Parsing

16. Lex. Semantics 14. Semantics

17. WSD and IR 15. Sem. Analysis

18. Discourse 19. Dialogue

20. Generation 21. Mach. Transl.

21. Mach. Transl.

Selected chapters from the book could also be used to augment courses in Artificial Intelligence, Cognitive Science, or Information Retrieval. --This text refers to an out of print or unavailable edition of this title.

See all Editorial Reviews

Product Details

• Hardcover: 1024 pages
• Publisher: Pearson Prentice Hall; 2 edition (May 26, 2008)
• Language: English
• ISBN-10: 0131873210
• ISBN-13: 978-0131873216
• Product Dimensions: 7 x 1.6 x 9.2 inches
• Shipping Weight: 3 pounds (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (33 customer reviews)
• Amazon Best Sellers Rank: #187,885 in Books (See Top 100 in Books)
  • #7 in Books > Computers & Technology > Software > Voice Recognition
  • #8 in Books > Computers & Technology > Software > Business > Speech Processing
  • #15 in Books > Computers & Technology > Software > Natural Language Processing

Most Helpful Customer Reviews

126 of 127 people found the following review helpful

5.0 out of 5 stars A Landmark Book September 12, 2000

By Peter Norvig

Format:Library Binding

The previous best book on NLP was James Allen's (1995), which was considered ambitious at the time because it covered syntax, semantics and some pragmatics. But Martin and Jurafsky is far more ambitious, because it covers speech recognition as well, and has far expanded coverage of language generation and translation. It also covers the great advances in statistical techniques that have marked the last decade. It is a beautiful synthesis that will reward the experienced expert in the field with new insights and new connections in the form of historical notes that are not well known. And it is well-written and clear enough that even the beginning student can follow it through. Before this book, you would have had to read Allen's book, Charniak's short book on statistical NLP, something on speech recognition, and something else on generation and translation. Like squeezing clowns into a circus car, Jurafsky and Martin somehow, improbably, manage to squeeze this all into one book, but in a way that is elegant and holds together perfectly; not at all the hodge-podge that one might expect. I expect that this book will be seen as one of the landmarks that pushes the field forward.

It's worth comparing this book to the other recent NLP text: Manning and Shutze. Jurafsky and Martin cover much more ground, including many aspects that are ignored by Manning and Schutze. So if you want a general overview of natural language, if you want to know about the syntax of English, or the intricacies of dialog, if you are teaching or taking a general NLP course, then Jurafsky and Martin is the one for you. But if your needs are more focused on the algorithms for lower-level text processing with statistical techniques, or if you want to build a specific practical application, then Manning and Schutze is far more comprehensive and likely to have your answer. If you're a serious student or professional in NLP, you just have to have both.

29 of 29 people found the following review helpful

3.0 out of 5 stars Good description of the problems in the field, but look elsewhere for practical solutions April 2, 2009

By P. Nadkarni

Format:Hardcover

The authors have the challenge of covering a vast area, and they do a good job of highlighting the hard problems within individual sub-fields, such as machine translation. The availability of an accompanying Web site is a strong plus, as is the extensive bibliography, which also includes links to freely available software and resources.

Now for the negatives.

While I would still buy and recommend this book, you will need to supplement it with other material; in addition to the accurate "broad and shallow" comment made by another reviewer, I would add that much of the material, as presented, is aimed at the comprehension level of a computer-science PhD and doesn't really meet the definition of a textbook for either undergraduate or graduate students. It is not that the material is intrinsically difficult: one recurring problem in the book is the vast number of forward references, where a topic is introduced very briefly but not explained until 20-50 pages later. In most cases, if you don't understand a passage in the text, I would advise that you keep skimming ahead - you may be rewarded because in several cases, the book covers a particular approach for 2-3 pages before telling you that its underlying assumptions are flawed, and that modern methods for addressing the problem use alternative approaches.

In other cases, the authors try to explain topics that might deserve entire chapters in about ten lines - a poster child is the explanation on page 736 of how Support Vector Machines can be used for multiclass problems. To someone who is familiar with SVMs, this material is unnecessary, while those who are not will not be enlightened by knowing that SVMS are "binary approaches based on the discovery of separating hyperplanes". I understand that this is not a text on machine learning approaches, even though machine-learning approaches have revolutionized NLP, but if the authors are clearly in no position to do justice to a particular topic in limited space, I would have preferred that they do the reader the courtesy of acknowledging the same, and simply point to a useful source, preferably online. (While the Wikipedia entry on SVMs is, as of this writing,incomprehensible to non-Math PhDs, the 2nd Google link, at www.dtreg.com, provides a reasonable overview.)

On the other hand, in a book that has to cover a vast area in limited space, there is a surprising amount of repetition. The page-long explanation of F-measure, a statistic used to evaluate the accuracy of a method, is repeated in three places almost verbatim, on pg. 455, 479 and 733; the repetition 24 pages apart (in chapters 13 and 14) should be considered astonishing given that the same author in the two-author collaboration clearly wrote both passages.

Finally, given the way algorithms are described - some reviewers point to errors in some of the descriptions, but I can't verify this - you would be hard-pressed to complete many of the exercises that follow each chapter, in terms of being able to implement a working program.

A final word of advice to the authors: I really do want to see a Third Edition, but I would recommend that you beta-test your material on a sample of your target audience, and incorporate their feedback. When you write a textbook, you really need to make a serious effort to communicate: if smart undergraduates or grad students tell you certain material is hard to follow, the fault almost certainly lies with you and not them.

21 of 23 people found the following review helpful

4.0 out of 5 stars Good, but many errors May 19, 2002

By A Customer

Format:Library Binding

This book is a great general introduction to NLP, covering a broad range of topics. Unfortunately there are many errors in the mathematical formulae and the algorithm descriptions, so do make sure to download the errata list from the book's home page.