Foundations of Statistical Natural Language Processing 1st Edition

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.

Unlike some of the reviewers here, my knowledge of NLP is acquired on the job and is focused more on technique and less on theory. I initially resisted buying this book because of the price and bought other (cheaper and more technique-oriented) books instead. After buying and reading the book, I think that its worth every penny. The book is really comprehensive, it covers in great detail all the techniques I know (and know I need to know). The math behind the algorithms are well explained, and allows you to generalize the ideas presented to new problems. Overall an excellent book, definitely something you should consider acquiring sooner rather than later if you are serious about NLP.

This is a good book for people who are interested in computational linguists, machinelearning 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.

This book was used in a course on natural language processing in computer science. We only cover a sliver of the content presented in this textbook. This book has tons of information and with much detailed information. The author did a great job covering almost all aspects of natural language processing as well as it's state in computing. I would recommend this book to anyone who is serious in learning natural language processing whether you are a linguist or a computer scientist.

A very useful and practical book on text-mining. I love the way its content is organized and the language is very clear. It is quite "easy" to understand (comparing to other text on NLP) and quite easy to convert the knowledge in this book to algorithms in your code. Highly recommend it if you consider getting started on text mining or general natural language processing.

This book will equip you with the knowledge and skill to tackle natural language processing problems.The clarity of the exposition makes the theory appear very simple and easy to apply.

I purchased this textbook initially for a class in statistical natural language processing in the Biomedical Informatics domain. Throughout the semester, it provided itself as a excellent reference text and also an added bonus of providing problems that challenged me quite thoroughly. I would suggest this text as a must have if you are interested in the realm of natural language processing.

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.