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Speech and Language Processing, 2nd Edition [Hardcover]

Daniel Jurafsky , James H. Martin
4.1 out of 5 stars  See all reviews (15 customer reviews)

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Book Description

May 16, 2008 0131873210 978-0131873216 2nd

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.


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Editorial Reviews

About the Author

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.


Product Details

  • Hardcover: 1024 pages
  • Publisher: Prentice Hall; 2nd edition (May 16, 2008)
  • Language: English
  • ISBN-10: 0131873210
  • ISBN-13: 978-0131873216
  • Product Dimensions: 9.2 x 7.3 x 1.6 inches
  • Shipping Weight: 3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (15 customer reviews)
  • Amazon Best Sellers Rank: #193,110 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
38 of 38 people found the following review helpful
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".
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4 of 4 people found the following review helpful
5.0 out of 5 stars Great introductions and reference book August 9, 2008
By carheg
Format:Hardcover
I read the first edition of that book and it is terrific. The second edition is much more adapted to current research. Statistical methods in NLP are more detailed and some syntax-based approaches are presented. My specific interest is in machine translation and dialogue systems. Both chapters are extensively rewritten and much more elaborated. I believe this book is perfect for everyone who starts in speech and language processing. With precision, coherent examples and some humor, this book give a great introduction into this topic as well as material for already experienced readers.
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3 of 3 people found the following review helpful
3.0 out of 5 stars Encyclopedic Treatment of NLP April 25, 2012
Format:Hardcover|Verified Purchase
Daniel Jurafsky and James Martin have assembled an incredible mass of information about natural language processing. The authors note that speech and language processing have largely non-overlapping histories that have relatively recently began to grow together. They have written this book to meet the need for a well-integrated discussion, historical and technical, of both fields.

In twenty-five chapters, the book covers the breadth of computational linguistics with an overall logical organization. Five chapter groupings organize material on Words, Speech, Syntax, Semantics and Pragmatics, and Applications. The four Applications chapters address Information Extraction, Question Answering and Summarization, Dialogue and Conversational Agents, and Machine Translation. The book covers a lot of ground, and a fifty-page bibliography directs readers to vast expanses beyond the book's horizon. The aging content problem present in all such books is addressed through the book's web site and numerous links to other sites, tools, and demonstrations. There is a lot of stuff.

While it is an achievement to assemble such a collection of relevant information, the book could be more useful than it is. An experienced editor could rearrange content into a more readable flow of information and increase the clarity of some of the authors' examples and explanations. As is, the book is a useful reference for researchers and practitioners already working in the field. A more clear presentation would lower the experience requirement and make its store of information available to students and non-specialists as well.
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4 of 5 people found the following review helpful
5.0 out of 5 stars Excellent Introduction to NLP June 29, 2010
Format:Hardcover
I'm in middle of reading this book as an introduction to NLP without a teacher, and I find it very clear, easy to read, and informative. I can't say that I know it covers the field well because I don't know about the field, but it seems to me to be quite thorough. Definitely recommended.
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1 of 1 people found the following review helpful
5.0 out of 5 stars Jurafsky and Martin March 14, 2014
Format:Hardcover|Verified Purchase
I give J&M five stars and they deserve it, and here’s why. If you want learn to write natural language software, no other single book is as good – at least I’ve not found it. In fact, I bet they invented the genre. Pulling this together is not easy, and they do a creditable job. I know a lot more than I did before I read this book, and I’ve been writing linguistic software for over 30 years. As a linguist writing software (as opposed to the other way around), one can feel just a tad under siege these days. Google advertises that they don’t have a single linguist on staff, and MS is ubiquitously quoted for saying that the quality of their software decreases for every linguist they hire… J&M, I’m happy to say, are above the fray. (What is ‘supervised’ machine learning? Oh yeah, that’s where your input was created by a linguist. Supervised or not, you’re just playing number games on the foundation of a theoretical framework invented by linguists.) They provide a balanced account with historical perspective. I like them. They’re cool.

So on to picking nits... which is way more fun. What I really wanted is to read this book and then be able to sit down and write my own Python implementation of the forward/backward algorithm to train an HMM. I bobbed along through the book, perhaps experiencing a little bit of fuzziness around those probabilities, and came full stop at ‘not quite ksi’ right smack in the middle of my HMM forward/backward section. I’d done a practice run by training a neural net in Andrew Ng’s machine learning course with Coursera. But I stared pretty hard for 3-4 hours at pages 189 and 190. And I mean I get it basically… Alpha and beta represent the accumulated wisdom coming from the front and from the back… And then you take a kind of average to go from not quite ksi to ksi.
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Most Recent Customer Reviews
4.0 out of 5 stars Foundation Textbook for NLP
I purchased this textbook initially for a class in natural language processing in the Biomedical Informatics domain. Read more
Published 5 months ago by Amol Bhalla
5.0 out of 5 stars A thorough book on NLP
Needless to say, this is a classic in the NLP domain. It is different with most of other NLP book in that it focuses "real" computational linguistics but tons of other... Read more
Published 15 months ago by Marlin
5.0 out of 5 stars Great book that covers a wide range of NLP
I purchased this book as a companion to the free Stanford NLP class being taught by Dan Jurafsky and Chris Manning. Read more
Published on March 14, 2012 by GeooeG
3.0 out of 5 stars Looks like a good book
I'm almost a quarter of the way through this book and am very happy with it so far. It covers a lot of territory, including both text and speech. Read more
Published on February 9, 2012 by EdK
5.0 out of 5 stars Fast and Good
Everything happened very smoothly. I received the book on time. Overall it was a very good service.
I am happy with this provider.
Published on October 15, 2011 by RuTX
5.0 out of 5 stars Pricey, but excellent textbook
I had to buy this textbook for my NLP class. I wasn't thrilled about needing to purchase this book due to its price, but I have been pleasantly surprised by the excellent writing. Read more
Published on September 26, 2011 by C. Matheson
5.0 out of 5 stars Broadest coverage with enough direction for further study
This is one of the books that I consider as a starting point / reference whenever I need to deal with a practical natural language processing (NLP) problem. Read more
Published on April 17, 2011 by Emre Sevinc
1.0 out of 5 stars Not really an "introduction"...
The dictionary defines "introduction" to a subject as "an elementary treatise", which this book most definitely is not. Read more
Published on November 3, 2010 by ShoppingGeek
4.0 out of 5 stars Amazing introduction book
Speech and Language Processing is an amazing book. It is a great introduction to NLP technology, with some notes about IR and IE. Read more
Published on July 15, 2009 by Alberto Planas Dominguez
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