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Foundations of Statistical Natural Language Processing [Hardcover]

Christopher Manning , Hinrich Schuetze
4.7 out of 5 stars  See all reviews (10 customer reviews)
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Book Description

May 28 1999 0262133601 978-0262133609 1

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

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Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days. Anyone who wants to learn this field would be well advised to get this book. For that matter, the same goes for anyone who is already in the field. I know that it is going to be one of the most well-thumbed books on my bookshelf.

(Eugene Charniak, Department of Computer Science, Brown University)

About the Author

Christopher D. Manning is Assistant Professor in the Department of Computer Science at Stanford University. Hinrich Schütze is on the Research Staff at the Xerox Palo Alto Research Center.

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THE AIM of a linguistic science is to be able to characterize and explain the multitude of linguistic observations circling around us, in conversations, writing, and other media. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews

4.7 out of 5 stars
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5.0 out of 5 stars Fantastic return on investment Sept. 12 2000
There are lots of books (and even more junk email) with titles like "Get Rich Quick". On the surface, this book is the exact opposite: a scholarly, scientific text aimed at comprehensive, accurate description, not at commercial hype. But if someone told me I had to make a million bucks in one year, and I could only refer to one book to do it, I'd grab a copy of this book and start a web text-processing company. Your return on investment might not be $1M, but this book delivers everything it promises. For all the major practical applications of statistical text processing, this book accurately and clearly surveys the major techniques. It often has pretty good advice about which techniques to prefer, but sometimes reads more like a catalog of listings (this reflects not on the authors' failing, but rather on the field's immaturity).
It's worth comparing this book to the other recent NLP text: Jurafsky and Martin's. (Disclaimer: I worked with them on the preparation of their text.) Jurafsky and Martin cover much more ground, including many aspects that are ignored by Manning and Schutze. So if you want a general overview of natural language, if you want to know about the syntax of English, or the intricacies of dialog, then Jurafsky and Martin is for you. But if your needs are more focused on the algorithms for lower-level text processing with statistical techniques, then Manning and Schutze is far more comprehensive. If you're a serious student or professional in NLP, you just have to have both.
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5.0 out of 5 stars An absolute MUST for anyone interested in NLP. May 26 1999
This is the best book I've ever read on computational linguistics. It should be ideal for both linguists who want to learn about statistical language processing and those building language applications who want to learn about linguistics. This book isn't even published and it's now my most highly used reference book, joining gems such as Cormen, Leiserson and Rivest's algorithm book, Quirk et al.'s English Grammar, and Andrew Gelman's Bayesian statistics book (three excellent companions to this book, by the way).
The book is written more like a computer science or math book in that it starts absolutely from scratch, but moves quickly and assumes a sophisticated reader. The first one hundred or so pages provide background in probability, information theory and linguistics.
This book covers (almost) every current trend in NLP from a statistical perspective: syntactic tagging, sense disambiguation, parsing, information retrieval, lexical subcategorization, Hidden Markov Models, and probabilistic context-free grammars. It also covers machine translation and information retrieval in later chapters.
It covers all the statistical techniques used in NLP from Bayes' law through to maximum entropy modeling, clustering: nearest neighbors and decision trees, and much more.
What you won't find is information on applications to higher-level discourse and dialogue phenomena like pronoun resolution or speech act classification.
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Compared to the slightly overrated Jurafsky and Martin's classic, this book aims less targets but hits them all more precisely, completely and satisfactory for the reader. That is, just to give an idea on what to expect, instead of attacking 200 problems on 2 pages each, it attacks only 40 problems on 10 pages each.
So read the TOC before you buy the book: if you find your topics there, you're done, you are saved, buy it and be happy. In contrast, you can buy Jurafsky's book without caring to read the TOC: you problem is likely to be mentioned there but it's quite unlikely to be detailed enough for your need.
Some introductory chapters take too much space and some advanced topics are missing. But the book is actually named "Foundations of..." so it seems to deliver precisely what it promisses, which is a precious and rare accomplishment by itself. I recommend this book, but read the TOC before you buy it!
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1 of 1 people found the following review helpful
5.0 out of 5 stars Better than Jurafsky and Martin Nov. 15 2002
If you can only own one book about statistical NLP, and the choice is down to this one or Jurafsky and Martin, choose this one. The mathematics is little more rigorous, but by no means daunting, and the exposition is clearer than J&M.
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5.0 out of 5 stars Which NLP techniques to apply? May 11 2001
If you need a good introductory textbook on NLP, look no further. While doing a project on information extraction of protein-protein interactions from biological free text, I was not sure which of the NLP grammar methods is relevant to the project. A web survey can give you a long listing of various grammar methods. To gain a sound background on how these grammar methods are related and evolved from one another, study chapters 11 and 12. The techniques used in some successful commercial products are discussed especially in chapter 12.2. With this book, it is unlikely that you will get lost when reading " Survey of the State of the Art in Human Language Technology" ([...]
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