- Series: Practical Resources for the Mental Health Professionals
- Hardcover: 934 pages
- Publisher: Prentice Hall; 1st edition (February 5, 2000)
- Language: English
- ISBN-10: 0130950696
- ISBN-13: 978-0130950697
- Product Dimensions: 7 x 1.9 x 9.2 inches
- Shipping Weight: 2.8 pounds
- Average Customer Review: 21 customer reviews
- Amazon Best Sellers Rank: #1,071,109 in Books (See Top 100 in Books)
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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition 1st Edition
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... 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
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:
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:
1 quarter NLP
1 semester Speech + NLP
1 semester Comp. Linguistics
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.
Top customer reviews
Hopefully the new Edition, has taken care of this short coming.
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.
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. Algorithms are given in well defined and carefully crafted pseudo-code (using pseudocode neatly leapfrogged two decades of computational linguistics books tied to obscure programming languages). For instance, their definition of CYK parsing is a minimal, elegant nesting of for-loops from which the complexity of the algorithm is self-evident. Speaking of rigor, the book is very well copy edited, typeset, and indexed.
This book isn't the last book you'll need; it's the first. Jurafsky and Martin open the door to the cognitive sciences, including linguistics, psychology and philosophy, and the computer sciences including logic, automata, formal languages, algorithms, and statistical estimation. Not to mention artificial intelligence; all the good problems are AI-complete**, after all, and Jurafsky and Martin don't let you forget it.
* There were actually several other chapter authors, including Keith Vander Linden on Natural Language Generation, Nigel Ward on Machine Translation, and Andy Kehler on Discourse; it's a tribute to all of them that the book hangs together so well.
** "AI-complete", a term derived from "NP-complete" and "Turing-complete", applies to a problem that is so hard that if you solved it, you could solve any other interesting artificial intelligence problem in terms of the solution to your problem.