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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition Library Binding – February 5, 2000

ISBN-13: 978-0130950697 ISBN-10: 0130950696 Edition: 1st

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Product Details

  • Series: Practical Resources for the Mental Health Professionals
  • Library Binding: 934 pages
  • Publisher: Prentice Hall; 1st edition (February 5, 2000)
  • Language: English
  • ISBN-10: 0130950696
  • ISBN-13: 978-0130950697
  • Product Dimensions: 9.3 x 7 x 1.8 inches
  • Shipping Weight: 2.8 pounds
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #222,688 in Books (See Top 100 in Books)

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

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.


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Customer Reviews

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If you're a serious student or professional in NLP, you just have to have both.
Peter Norvig
This book provides an excellent comprehensive text on natural language processing and computational linguistics.
Chad Schoettger
They make useful references to popular movies and culture without sacrificing their academic reputation.
maiku

Most Helpful Customer Reviews

128 of 130 people found the following review helpful By Peter Norvig on September 12, 2000
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.
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21 of 23 people found the following review helpful By A Customer on May 19, 2002
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.
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27 of 31 people found the following review helpful By Sean A. Fulop on May 19, 2005
Format: Library Binding Verified Purchase
This book is by now an accepted classic in the field. It is basically the only textbook that covers so much of computational linguistics, so I have had no choice but to use it for the past several years. Just the same, I'd rather not use it for teaching linguistics students. While the book has much to offer the professional, including a broad range of topics extensively researched, it is much more useful in this "handbook" capacity than as a textbook for the uninitiated. The chief reasons for this are: 1) It is pedagogically very poor; the majority of concepts are either explained in a confusing and obfuscatory manner or are not explained and are simply left in algorithmic form. This is not usually edifying to the linguistics student with no computer science background. 2) There are too many mistakes in its algorithms and method overviews. So far as I can see, even the famed Earley parsing algorithm is wrong here, it will not yield the correct output. 3) It is not written in a language that linguistics students can understand. With no background in mathematics, computer science, or pseudocode, such students need much more coddling than is provided by this book, and they are virtually unable to read it. Basically, as the title to this review states, what is called for now is a book to explain the contents of this book. Perhaps if my students keep encouraging me to write it. . .
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35 of 42 people found the following review helpful By Peter Alfheim on May 25, 2002
Format: Library Binding Verified Purchase
GENERAL IDEA: Broad coverage, it lacks depth and details - particularly practical details. That is, the presentation is often sketchy, mainly because it approaches too many subjects for its available space. I would not say that this book is strong on theory either. It is quite obvious that it avoids getting too formal and precise, probably to remain attractive for non-specialists too.

CASE STUDY: One specific problem I had with the Hidden Markov Models, that are supperficially presented (or spread I could say) in several separate sections of the book, so it's not been a pleasure trying to actually understand them properly and completely as a fundamental concept, to make them work in my particular application.

TITLE: The book's title IS misleading because it starts with "Speeech" and this book's main subject is not speech but (written) language. Actually there are only a few chapters on speech.

CONCLUSION: Get this book if you are looking for a good overview of the field. The book will introduce you to a thousand of topics. As soon as you need in-depth coverage of some particular topic, you will look for additional resources.
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8 of 8 people found the following review helpful By maiku on November 23, 2000
Format: Library Binding
I started reading James Allen's Natural Language Understanding to get background information on an NLP indepedent study project. The book was good, but I still found some points unclear and turned to Jurafsky/Martin for more information. In the end I found Jurafsky very comprehensive and much more down to earth than Allen. (They make useful references to popular movies and culture without sacrificing their academic reputation.) The work introduces basic NLP concepts as Allen does, but then presents applications that continually refer back to the methods. For example, Allen explains the Viterbi algorithm as a method for tagging sentences. Jurafsky/Martin present it, then refer to it in applications such as spell checking, voice recognition, and sentence tagging. The book also serves as a useful guide to finding the more significant NLP papers for further research. If you're interested in NLP this is an excellent place to start!
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