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Foundations of Statistical Natural Language Processing Hardcover – May 28 1999
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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.
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)
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
Top Customer Reviews
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.
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.
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!
Most recent customer reviews
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 morePublished on Aug. 22 2002 by Emmanuel Lambert
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 morePublished on Nov. 22 2000 by Chris McKinstry
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