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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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Review

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.

Inside This Book (Learn More)
First Sentence
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
4.7 out of 5 stars
Most helpful customer reviews
1 of 1 people found the following review helpful
5.0 out of 5 stars Better than Jurafsky and Martin Nov. 15 2002
Format:Hardcover
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 very definitive, really a must read Sept. 15 2003
By A Customer
Format:Hardcover
this is an import pre-req to any research/inquiry into this field.
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3.0 out of 5 stars Very technical Aug. 22 2002
Format:Hardcover
Only buy this book if you want a very technical book about this subject. I bought this book because I was generally interested in this research field... and I never read it. If you are a researcher or a student studying this field, then this might be a good book. Otherwise, there are books that you will probably enjoy more.
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Format:Hardcover
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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4.0 out of 5 stars Perhaps looking somewhere else might help.. Dec 27 2001
Format:Hardcover
I was likely spoiled by some great course notes (courtesy of Jan Hajic). So when I found Manning and Schutze to be of little help, it was likely because it was too much of an introduction and didn't have the full discussions that I needed. Yet, later on I found that the text by Jurafsky and Martin succeeded in ways Manning and Schutze failed. Therefore, any individual interested in getting a good (and full) introduction to NLP should perhaps look at the Jurafsky and Martin, along with Manning and Schutze. But the breadth of Manning and Schutze and its place as a standard warrants at least 4 out of 5 stars (its not a terrible or mediocore book)
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