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Speech and Language Processing, 2nd Edition, by Daniel Jurafsky, James H. Martin
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For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing.
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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 text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.
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Supplements:
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Click on the "Resources" tab to View Downloadable Files:
- Solutions
- Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13�and 24 Now Available!
- For additional resourcse visit the author website: http://www.cs.colorado.edu/~martin/slp.html�
- Sales Rank: #78031 in Books
- Brand: Jurafsky, Daniel/ Martin, James H.
- Published on: 2008-05-16
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x 1.50" w x 7.20" l, 3.53 pounds
- Binding: Hardcover
- 1024 pages
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.
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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.
Most helpful customer reviews
9 of 9 people found the following review helpful.
Jurafsky and Martin
By Margaret Magnus
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. But there are too many assumptions hidden in P(X,Y|Z)/P(Y|Z). And this is an iterative algorithm, so how do you seed the counts? And I’m very annoyed by the phrase ‘note the different conditioning of O’. Okay, I can see the O is on the wrong side of the line. What does that mean? When I came to the next impasse, I didn’t try as hard. It’s already clear I’ll have to go elsewhere for the silver bullet. (The next impasse, btw was the cepstrum – what do you mean you leave the graph the same and just replace the x-axis with something totally unrelated? I’m no Stanford professor, but what kind of math is that? I’m sure it means something to somebody, but not to me.)
And drop the pseudo-code. If you’re deadly serious about teaching me the HMM, then write out a working implementation in full in a real language like C or Python with the variables all initialized so I can copy and paste the code into my debugger and watch what happens to the numbers as I step through. I suspect J&M of compromising the pedagogical value of the book by deliberately withholding information from those brilliant Stanford students of theirs so they have something to quiz them on at the end of the chapter. But this is a mistake. Give us the answers. Give us all the answers. Give us the actual code for the HMM and then explain it. I will read the explanation. I’ll have to read the explanation, because my neck is on the line if my code blows up. There will still be plenty of questions left over for those students.
61 of 62 people found the following review helpful.
Good description of the problems in the field, but look elsewhere for practical solutions
By P. Nadkarni
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.
10 of 11 people found the following review helpful.
Encyclopedic Treatment of NLP
By John M. Ford
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 Sch�tze'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.
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