Foundations of Statistical Natural Language Processing

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.


An absolute MUST for anyone interested in NLP.   May 26, 1999
Bob Carpenter (carp@research.bell-labs.com) (Murray Hill, NJ)
117 out of 118 found this review helpful

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.


Self-contained and instructive, read the TOC first!   May 26, 2002
Peter Alfheim (United States)
41 out of 43 found this review helpful

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.


Complete & Self-Contained   November 22, 2000
Chris McKinstry (South America)
10 out of 11 found this review helpful

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 more importantly the corpa, to prove this to be the case.

This is the post-rationalist, post-Chomskian age, and this book is a complete and self-contained introduction to the emperical methods of statistical natural lanagage processing that define it.

If you want in to this field, this is the door.


Which NLP techniques to apply?   May 12, 2001
Kah Tong, Seow (Singapore)
7 out of 8 found this review helpful

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" (http://cslu.cse.ogi.edu/HLTsurvey/HLTsurvey.html)