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Foundations of Statistical Natural Language Processing (Hardcover)

by Christopher D. Manning (Author), Hinrich Schuetze (Author)

4.6 out of 5 stars See all reviews (11 customer reviews)

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Editorial Reviews

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

Product Description
"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

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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Product Details

Hardcover: 620 pages
Publisher: The MIT Press; 1 edition (June 18, 1999)
Language: English
ISBN-10: 0262133601
ISBN-13: 978-0262133609
Product Dimensions: 9.1 x 7.9 x 1.3 inches
Shipping Weight: 2.9 pounds (View shipping rates and policies)

Average Customer Review: 4.6 out of 5 stars See all reviews (11 customer reviews)

Amazon.com Sales Rank: #110,243 in Books (See Bestsellers in Books)

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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

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
collocation discovery, term distribution models, selectional preference strength, unsupervised disambiguation, ten bigrams, unigram estimate, model taggers, finding collocations, stack decoding algorithm, different tag sets, one sense per discourse, most frequent tag, crossing brackets, noisy channel model, selectional preferences, model tagging, probabilistic parsing, maximum entropy modeling, aligned corpus, most likely parse, trigram model, verb attachment, aligning sentences, bigram model, probability parse

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Naive Bayes, New York Times, Linguistic Essentials, Mathematical Foundations, Exercises Exercise, Tom Sawyer, Probabilistic Context Free Grammars, Simplified Polynesian, San Francisco, Wall Street, York City, Minimum Description Length, Singular Value Decomposition, United States, University of Lancaster, Woody Allen, Ayatollah Ruhollah, Karim Obeid, Linguistic Data Consortium

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Citations (learn more)

This book cites 100 books:

Computational Linguistics: An Introduction (Studies in Natural Language Processing) by Ralph Grishman on 14 pages
Corpus Processing for Lexical Acquisition (Language, Speech, and Communication) by Branimir Boguraev on 9 pages
Lexical Acquisition: Exploiting On-line Resources To Build A Lexicon by Uri Zernik on 4 pages
Information Retrieval by C. J. Van Rijsbergen on page 570, page 572, and Back Matter
The Adventures of Tom Sawyer (Penguin Classics) by Mark Twain on page 20, and page 33

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Customer Reviews

11 Reviews

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4 star:		(2)
3 star:		(1)
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Average Customer Review

4.6 out of 5 stars (11 customer reviews)

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Most Helpful Customer Reviews

119 of 120 people found the following review helpful:

5.0 out of 5 stars An absolute MUST for anyone interested in NLP., May 26, 1999

By	Bob Carpenter (carp@research.bell-labs.com) (Murray Hill, NJ) - See all my reviews

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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96 of 98 people found the following review helpful:

5.0 out of 5 stars Fantastic return on investment, September 12, 2000

By	Peter Norvig (Palo Alto, CA USA) - See all my reviews (REAL NAME)

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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43 of 45 people found the following review helpful:

5.0 out of 5 stars Self-contained and instructive, read the TOC first!, May 25, 2002

By	Peter Alfheim (United States) - See all my reviews

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.

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Most Recent Customer Reviews

4.0 out of 5 stars Good book for people interested in Natural Language Processing.
This is a good book for people who are interested in computational linguists, machine
learning experts who are looking for new application domains and in general for someone... Read more

Published 22 months ago by Vishnu Vyas Sethumadhavan

5.0 out of 5 stars fastest delivery
I have never received anything so quick buying off the internet. Few days and I had the book in my hand. I was pleasantly surprised.

Published on July 3, 2005 by Sudhir Shakya

5.0 out of 5 stars very definitive, really a must read
this is an import pre-req to any research/inquiry into this field.

Published on September 15, 2003

3.0 out of 5 stars Very technical
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. Read more

Published on August 22, 2002 by Emmanuel Lambert

4.0 out of 5 stars Perhaps looking somewhere else might help..
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... Read more

Published on December 27, 2001 by Tahir Butt

5.0 out of 5 stars Which NLP techniques to apply?
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... Read more

Published on May 11, 2001 by Kah Tong, Seow

5.0 out of 5 stars Complete & Self-Contained
In 1957, J. R. Firth coined the phrase "You shall know a word by the company it keeps", unfortunately it's taken almost four decades for us to create the technology and... Read more

Published on November 22, 2000 by Chris McKinstry

5.0 out of 5 stars Makes a great textbook...
My professor chose this book for a undergraduate course in Statistical Natural Language Processing and as a student I found it to be a great learning tool. Read more

Published on April 8, 2000

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