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 ofthe most fast-moving and exciting areas of computer science thesedays. Anyone who wants to learn this field would be well advised toget this book. For that matter, the same goes for anyone who isalready in the field. I know that it is going to be one of the mostwell-thumbed books on my bookshelf." Eugene Charniak , Department of Computer Science, Brown University
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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
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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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Most Helpful Customer Reviews
20 of 20 people found the following review helpful
By A Customer
Format:Hardcover
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.
2 of 6 people found the following review helpful
1.0 out of 5 stars
Many errors, unclear and "circular" explanations, few intuitive explanations
21 May 2008
By HerrKaputt
Format:Hardcover
I think this book is not good enough to be recommended. It has a lot of irrelevant writing (i.e. many paragraphs which do not provide any new information, or interpretation value), explanations are typically "circular" instead of simple, intuitive and to the point, and it has many errors. One such error is absolutely terrifying:
"PCA can only be applied to a square [data] matrix" (page 556 in the second edition from 2000)
In other words, you can only apply PCA if you have as many data points as dimensions of the input space! All the people who I showed this have laughed in response, and this actually convinced my NLP professor to look for another course book.
I got the book from the university library, and I usually consider buying the books I use. I am SO glad I didn't buy this one. I wouldn't pay even 1 euro for it, let alone almost 75 which is the typical price.
Bottom line: don't get it.
"PCA can only be applied to a square [data] matrix" (page 556 in the second edition from 2000)
In other words, you can only apply PCA if you have as many data points as dimensions of the input space! All the people who I showed this have laughed in response, and this actually convinced my NLP professor to look for another course book.
I got the book from the university library, and I usually consider buying the books I use. I am SO glad I didn't buy this one. I wouldn't pay even 1 euro for it, let alone almost 75 which is the typical price.
Bottom line: don't get it.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4.7 out of 5 stars
15 reviews
134 of 135 people found the following review helpful
5.0 out of 5 stars
An absolute MUST for anyone interested in NLP.
26 May 1999
By Bob Carpenter (carp@research.bell-labs.com)
- Published on Amazon.com
Format:Hardcover
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.
125 of 127 people found the following review helpful
5.0 out of 5 stars
Fantastic return on investment
13 Sep 2000
By Peter Norvig
- Published on Amazon.com
Format:Hardcover
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.
53 of 55 people found the following review helpful
5.0 out of 5 stars
Self-contained and instructive, read the TOC first!
26 May 2002
By Peter Alfheim
- Published on Amazon.com
Format:Hardcover|Amazon Verified Purchase
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 you an idea on what to expect, instead of attacking 200 problems on 2 pages each, this book 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: your problem is likely to be mentioned there but it's quite unlikely to be detailed enough to satisfy you.
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.
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: your problem is likely to be mentioned there but it's quite unlikely to be detailed enough to satisfy you.
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.
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4.7 out of 5 stars
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