Free PDF Speech and Language Processing, 2nd Edition

Free PDF Speech and Language Processing, 2nd Edition

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Speech and Language Processing, 2nd Edition

Free PDF Speech and Language Processing, 2nd Edition

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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.

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Product details

Hardcover: 1032 pages

Publisher: Prentice Hall; 2nd edition (May 16, 2008)

Language: English

ISBN-10: 9780131873216

ISBN-13: 978-0131873216

ASIN: 0131873210

Product Dimensions:

7.4 x 1.6 x 9.6 inches

Shipping Weight: 3.4 pounds (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

24 customer reviews

Amazon Best Sellers Rank:

#453,237 in Books (See Top 100 in Books)

A wonderful book which is used in many Natural Language Processing courses. It covers a huge number of topics, and goes quite deeply into each of them. I didn't intent to purchase this book at first but when I realized how useful it would be to have a physical copy with me, I was not hesitate to get one.

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.

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 books focus on some toolkit or practical methodologies. The book is thorough and comprehensive and suitable for all levels of learners.

While I'm only six or seven chapters into it, which are of an introductory nature, so far the book is excellent. For anyone interested in automated processing of natural speech, I think this would be a terrific addition. I'm learning a great deal from it, which is very much cementing my foundation in these concepts.

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.

I purchased this textbook initially for a class in natural language processing in the Biomedical Informatics domain. Throughout the semester, it provided itself as a excellent reference text and also an added bonus of providing problems that challenged me quite thoroughly. I would suggest this text as a must have if you are interested in the realm of natural language processing and speech processing.

This is the best book of all time. If you want to learn everything NLP get this!

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Speech and Language Processing, 2nd Edition PDF

Speech and Language Processing, 2nd Edition PDF

Speech and Language Processing, 2nd Edition PDF

Speech and Language Processing, 2nd Edition PDF