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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.
Top Customer Reviews
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".Read more ›
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.Read more ›
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 ›
Most Recent Customer Reviews
This book introduces readers to important topics in NLP. In places where it needs to go deeper it seems like it compiles information from relevant published papers and provides... Read morePublished 9 months ago by Renat Bekbolatov
It can be dense sometimes, but it's one of the most helpful textbooks I've had for computational linguistics. Read morePublished 16 months ago by Vanessa A.
I love this book.
It was easy to follow and a great read.
I needed the book for my natural language processing class. needless to say, I learnt a lot.Published 21 months ago by Kamran
I purchased this textbook initially for a class in natural language processing in the Biomedical Informatics domain. Read morePublished on January 18, 2014 by Amol Bhalla
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... Read morePublished on April 15, 2013 by Marlin
I purchased this book as a companion to the free Stanford NLP class being taught by Dan Jurafsky and Chris Manning. Read morePublished on March 14, 2012 by GeooeG
I'm almost a quarter of the way through this book and am very happy with it so far. It covers a lot of territory, including both text and speech. Read morePublished on February 9, 2012 by EdK
Everything happened very smoothly. I received the book on time. Overall it was a very good service.
I am happy with this provider.