Customer Reviews

Foundations of Statistical Natural Language Processing

5 star:		(10)
4 star:		(2)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

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.

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.

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.

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.

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)

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 who wants an introduction to statistical computational linguistics.

The book is self contained and very well written. It treats most of the general statistical approaches to language processing such as language models, smoothing, etc.. in an excellent, but introductory manner. The book is a good start for any one looking to enter statistical nlp, however for advanced readers who would like to see the cutting edge of statistical computational linguistics they should look somewhere else.

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. It gave sufficient background in statistics and language so people with little background in this areas can get up to speed quickly.

Lots of interesting assignments are proposed at the end of each chapter, and while some of the questions are rather vague (particularly with respect to the data they are refering to at times) they can be good starting points for further discussion or projects.

As a student, I give this book an A+.

I am particularly interested in the grammar trees and the Disambiguation algorithms. The chapter on data structures to store associations (meaning) is also very useful. I will use this book and others as the basis for designing a software project.

I purchased this book when designing my first POS tagger for English language.
Since my major is neither NLP, computer science nor mathematics, I had at first had
difficulty in genuinely understanding the core concepts, for example, modeling natural
language "event" such as tagging, parsing or translation, but my second reading
started to be paying off by reading this together with books on statistics, calculus, and algebra.

I recommend this book if you are a patient reader, since, as mentioned above, mathematical
background is prerequisite, which is explicitly written on the preface.

This book was especially helpful when writing POS tagger and syntactic parser. But if you are
eager to learn subject about translation decoder or word alignment model trainer, I
recommend that you also purchase Koehn's book.

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)