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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition Library Binding – Jan 26 2000


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Product Details

  • Library Binding: 934 pages
  • Publisher: Prentice Hall; US ed edition (Jan. 26 2000)
  • Language: English
  • ISBN-10: 0130950696
  • ISBN-13: 978-0130950697
  • Product Dimensions: 17.7 x 4.8 x 23.3 cm
  • Shipping Weight: 1.3 Kg
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Bestsellers Rank: #716,836 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

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

The field of human language processing encompasses a diverse array of disciplines, and as such is an incredibly challenging field to master. This book does a wonderful job of bringing together this vast body of knowledge in a form that is both accessible and comprehensive. Its encyclopedic coverage makes it a must-have for people already in the field, while the clear presentation style and many examples make it an ideal textbook. -- Eric Brill, Microsoft Research

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:

  1. 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.
  2. 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.
  3. 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).
  4. 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.

Customer Reviews

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Format: Library Binding
This book helped me accomplish what I set out to do; namely to obtain an overview of the field of natural language processing, with an emphasis on language understanding (as opposed to recognition). And I can recommend it on that level. The weakness of the book however is that it left me asking, "OK, now what?". The book started off strong with a number of dynamic-programming algorithms, finite automaton models, and N-grams that one could sink his/her teeth into from an algorithmic point-of-view. But when it came to actual techniques for natural-language understanding (chapters 14-17) the goods were not delivered. The algorithms disappeared, and the best I could find was in Chapter 15 an incomplete, and unconvincing treatment of Hiyan Alshawi's semantic parsing techniques which fueled the Core Language Engine last decade. Chapter 16 dealt with lexical semantics and was almost entirely devoid of algorithms.
My gut feeling after reading this text is that parsing techniques will likely give way to statistical and probabilistic learning methods that will in some sense bypass the need to correctly or accurately parse language. I cannot fault the authors for not exploring this in more depth,as this represents the cutting edge for both NLP and artificial intelligence. In any case, I'm off to read Schutze and Manning's book which will hopefully provide a bit more focus on that perspective. What intrigues me is that most people can understand some language, but very few people understand the grammar of their own language, especially if they have been deprived of a formal education. So why should computers need to know all about grammar rules and parsing? Could they instead be trained by simply being exposed to enough interactions between language and objects? I teach in a department dominated by both foreign and immigrant students. I understand them most of the time, but I would estimate that half the time their sentences or utterances would not fail to be parsed correctly.
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Format: Library Binding
GENERAL IDEA: Broad coverage but it lacks depth and details - particularly practical details. That is, the presentation is often too 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 rigurous, 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. As soon as you need in-depth coverage of some particular topic you will look for additional resources.
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Format: Library Binding
I recently had reason to return to Jurafsky and Martin's* "Speech and Language Processing" to do a little brush-up on pronunciation models. Of course, I got diverted; this time by an insightful review of the "internal structure" of words. I came away reminded of why this is perhaps the single best textbook I've ever read. "Speech and Language Processing" is always the first source I check, and it is quite often the last.
First of all, Jurafsky and Martin cover absolutely everything you need to know in order to understand the state of the art systems and to read primary sources such as journals or conference proceedings. You could teach an advanced undergraduate or graduate course by simply tackling it a chapter at a time and discussing everyone's solutions to the exercises. The book is organized by interleaving theoretical topics, such as regular expressions and automata, with practical applications, such as pronunciation modeling or pattern matching. This allows for a fast start on interesting and realistic applications while providing a solid foundation for understanding the field.
Second, the book is not only readable, it's enjoyable. The examples are clever, not cute or forced. The topics flow from one to the next in an almost seamless narrative.
Third, the book is scholarly to the point of lacing pages with references to original sources. Somehow, Jurafsky and Martin have managed to track down fascinating threads such as the development of the currently accepted statistical models for speech recognition.
Fourth, and most amazingly, Jurafsky and Martin manage all of this while maintaining a rigorous standard of definition and example that should be a model to the rest of the field. Terms are defined when they're used or cross-referenced.
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By Peter Norvig on Sept. 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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