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| Speech and Language Processing (2nd Edition) (Prentice Hall Series in Artificial Intelligence) | 
| Authors: Daniel Jurafsky, James H. Martin
Publisher: Prentice Hall
Category: Book
List Price: $118.00
Buy New: $93.71
You Save: $24.29 (21%)
Buy New/Used from $93.71
Avg. Customer Rating:  (20 reviews)
Sales Rank: 116928
Languages: English (Original Language), English (Unknown), English (Published)
Media: Hardcover
Edition: 2
Number Of Items: 1
Pages: 1024
Shipping Weight (lbs): 3.4
Dimensions (in): 9.4 x 7 x 1.7
ISBN: 0131873210
Dewey Decimal Number: 410.285
EAN: 9780131873216
ASIN: 0131873210
Publication Date: May 26, 2008
Availability: Usually ships in 1-2 business days
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Product Description
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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| Customer Reviews: Read 15 more reviews...
 quite good, but still has improvement space November 2, 2008
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.
 Great introductions and reference book August 9, 2008
3 out of 3 found this review helpful
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.
 Needs a second volume which explains the first May 19, 2005
10 out of 10 found this review helpful
This book is by now an accepted classic in the field. It is basically the only textbook that covers so much of computational linguistics, so I have had no choice but to use it for the past several years. Just the same, I'd rather not use it for teaching linguistics students. While the book has much to offer the professional, including a broad range of topics extensively researched, it is much more useful in this "handbook" capacity than as a textbook for the uninitiated. The chief reasons for this are: 1) It is pedagogically very poor; the majority of concepts are either explained in a confusing and obfuscatory manner or are not explained and are simply left in algorithmic form. This is not usually edifying to the linguistics student with no computer science background. 2) There are too many mistakes in its algorithms and method overviews. So far as I can see, even the famed Earley parsing algorithm is wrong here, it will not yield the correct output. 3) It is not written in a language that linguistics students can understand. With no background in mathematics, computer science, or pseudocode, such students need much more coddling than is provided by this book, and they are virtually unable to read it. Basically, as the title to this review states, what is called for now is a book to explain the contents of this book. Perhaps if my students keep encouraging me to write it. . .
I looked for November 5, 2003
2 out of 20 found this review helpful
something which I can use - I am a linguist - and found it immensly readable and useful
The a good introduction to NLP, but could be improved April 15, 2003
10 out of 12 found this review helpful
This book helped me accomplish what I set out to do; namely to obtain an overview of the field of natural language processing, with an emphasis on language understanding (as opposed to recognition). And I can recommend it on that level. The weakness of the book however is that it left me asking, "OK, now what?". The book started off strong with a number of dynamic-programming algorithms, finite automaton models, and N-grams that one could sink his/her teeth into from an algorithmic point-of-view. But when it came to actual techniques for natural-language understanding (chapters 14-17) the goods were not delivered. The algorithms disappeared, and the best I could find was in Chapter 15 an incomplete, and unconvincing treatment of Hiyan Alshawi's semantic parsing techniques which fueled the Core Language Engine last decade. Chapter 16 dealt with lexical semantics and was almost entirely devoid of algorithms. My gut feeling after reading this text is that parsing techniques will likely give way to statistical and probabilistic learning methods that will in some sense bypass the need to correctly or accurately parse language. I cannot fault the authors for not exploring this in more depth,as this represents the cutting edge for both NLP and artificial intelligence. In any case, I'm off to read Schutze and Manning's book which will hopefully provide a bit more focus on that perspective. What intrigues me is that most people can understand some language, but very few people understand the grammar of their own language, especially if they have been deprived of a formal education. So why should computers need to know all about grammar rules and parsing? Could they instead be trained by simply being exposed to enough interactions between language and objects? I teach in a department dominated by both foreign and immigrant students. I understand them most of the time, but I would estimate that half the time their sentences or utterances would not fail to be parsed correctly.
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