CDN$ 167.04
  • List Price: CDN$ 199.00
  • You Save: CDN$ 31.96 (16%)
Only 2 left in stock (more on the way).
Ships from and sold by Amazon.ca.
Gift-wrap available.
Quantity:1
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Speech and Language Processing (2nd Edition) Hardcover – May 16 2008


See all 2 formats and editions Hide other formats and editions
Amazon Price New from Used from
Hardcover
"Please retry"
CDN$ 167.04
CDN$ 42.99 CDN$ 145.93

2014 Books Gift Guide for Children & Teens
Browse our featured books to find gift ideas for the boys or girls on your holiday shopping list this year!

Frequently Bought Together

Speech and Language Processing (2nd Edition) + Foundations of Statistical Natural Language Processing
Price For Both: CDN$ 268.99


Customers Who Bought This Item Also Bought



Product Details

  • Hardcover: 1024 pages
  • Publisher: Prentice Hall; 2 edition (May 16 2008)
  • Language: English
  • ISBN-10: 0131873210
  • ISBN-13: 978-0131873216
  • Product Dimensions: 19 x 5.6 x 23.6 cm
  • Shipping Weight: 1.7 Kg
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #171,530 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

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.

Customer Reviews

4.0 out of 5 stars
5 star
1
4 star
0
3 star
1
2 star
0
1 star
0
See both customer reviews
Share your thoughts with other customers

Most helpful customer reviews

1 of 1 people found the following review helpful By John M. Ford TOP 100 REVIEWER on Feb. 23 2013
Format: Hardcover
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.
Read more ›
Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again.
By Syed Latifi on Nov. 2 2014
Format: Hardcover Verified Purchase
Excellent book, thorough!
Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again.

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 16 reviews
43 of 43 people found the following review helpful
Good description of the problems in the field, but look elsewhere for practical solutions April 2 2009
By P. Nadkarni - Published on Amazon.com
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 [...] 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.
6 of 6 people found the following review helpful
Encyclopedic Treatment of NLP April 25 2012
By John M. Ford - Published on Amazon.com
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.

Readers looking for an introduction to natural language processing might find Manning and Schütze's Foundations of Statistical Natural Language Processing, easier to understand. It is over ten years old, but worth reading for an understanding of basic concepts that are still relevant in the field.
4 of 4 people found the following review helpful
Great introductions and reference book Aug. 9 2008
By carheg - Published on Amazon.com
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.
5 of 6 people found the following review helpful
Excellent Introduction to NLP June 29 2010
By A student - Published on Amazon.com
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.
1 of 1 people found the following review helpful
Broadest coverage with enough direction for further study April 17 2011
By Emre Sevinc - Published on Amazon.com
Format: Hardcover Verified Purchase
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. I also have Natural Language Processing with Python on my shelf and it's wonderful in terms of providing a practical start for nearly any NLP problem but when the need arises to cover more ground both in terms of theory and practical pitfalls then Jurafsky & Martin is my guide.

Natural language processing is a fast-moving target and it is impossible to know about the latest developments in the field without reading recent academic articles so nobody should expect to get the same information from this book, however mastering the concepts and algoritmhs in the book will provide the reader with the necessary background to understand state-of-the-art in NLP.

Most of the exercises are very interesting but I wish they had some kind of difficulty level indicated next to them. Another criticism would be that more information on practical implementation details of the algorithms could have been given but I believe these minor criticisms does not lead to a four star rating. It is a very difficult project to give a comprehensive overview of the whole NLP field and Jurafsky & Martin achieved that.


Feedback