Speech and Language Processing (2nd Edition),   ISBN:9780131873216

Customer Rating: 

The only problem I have with this book is that a bit verbose. I also wish that it would get a bit more into the mathematics of HHMs and provide better examples.

Hopefully the new Edition, has taken care of this short coming.

Customer Rating: 

Speech and Language Processing is an amazing book. It is a great introduction to NLP technology, with some notes about IR and IE. It is a shame that this great book don't go deeper about some IA algorithm like SVM with applications in NLP. I think that this book, with "Introduction to Information Retrieval" (Manning, Raghavan and Shütze), are two greats books to read and study.

Customer Rating: 

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 www.dtreg.com, 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.

Customer Rating: 

As its lengthy subtitle suggests, this is a big book (just under a thousand pages) and unbelievably comprehensive. On the whole, the book is a major improvement over its predecessor. The first edition was plagued with typos on seemingly every page, and was also way too thin in certain places. I seem to remember them rushing through phonetics in a single page or two, and then describing optimality theory in just a couple sentences! The second edition's coverage of the field is significantly broader and deeper. Phonetics now gets a good 15 pages. The typos are gone and the appearance of the book is also much improved, with nice-looking black-and-white diagrams on nearly every page.

I have one pedagogical quibble with the new edition. The first edition introduced readers to the Bayesian noisy channel model by applying it to the problem of spelling correction, as implemented in the classic paper by Kernighan et al. Because noisy channel spelling correction is so fiendishly simple, and the paper is so readable, this was the perfect way to introduce a student to Bayesian models of language. In the second edition, however, the authors decided to jump straight into noisy channel POS tagging, a much more challenging topic, and to relegate spelling correction to an "Advanced" (?) section at the end of Chapter 5. They really should have started with spelling correction and then moved to tagging.

Quibbles aside, this book is a spectacular achievement. The first edition of Speech and Language Processing was a breathtaking synthesis of material, and it helped to unify the field of language technology, despite its flaws. This greatly updated second edition is a big improvement and will be the standard text in the field for years to come.

Customer Rating: 

This book is quite good if you are interested in NLP.
If the could author provide a CD with some demos,source codes and applications, it, the reader can understand the contend in this book easier.
This course is really realy difficult.