Foundations of Statistical Natural Language Processing 1st Edition by Christopher D. Manning (PDF)

Description

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.

User’s Reviews

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& quot; 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.& quot; — Eugene Charniak, Department of Computer Science, Brown University” 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– Eugene Charniak, Department of Computer Science, Brown University 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 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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.

⭐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.

⭐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 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.

⭐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.

⭐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.

⭐Great business!

⭐Besides just being outdated, this book can be super hard to understand at times. And it’s not just me: my classmates and professor agree. The authors rarely state things in an explicit way that makes sense, rather using dry and/or flowery language to disguise the fact that they sometimes don’t seem to know what they’re talking about. There are some good parts I guess, but generally it’s a difficult read with a lot of statistics and not much fundamentals. Only get this book if you have to.

⭐I had to buy the ebook again from MIT press as the formulae in the kindle version are unreadable. Thanks Amazon.

⭐Absolutely essential!

⭐Zusammen mit Jurafsky&Martin eines DER Standardwerke der Computerlinguistik,habe mir beide zu Beginn meines Computerlinguistik-Studiums gekauft.Jurafsky&Martin ist nicht nur hinsichtlich der Seitenzahl, sondern auch hinsichtlich der Themen deutlich umfangreicher,dafür gehen Manning&Schütze deutlich mehr in die (mathematischen) Details ausgewählter Themengebiete. Beide Bücher geben in den ersten Kapiteln eine sehr basale Einführung in linguistische Terminologie.Über das Studium hinweg habe ich deutlich häufiger in Jurafsky&Martin als in Manning&Schütze gelesen, Stichwort Themenreichtum, dennoch würde ich das Buch Computerlinguistik-Studenten empfehlen.Allerdings sollte auch nicht verschwiegen werden, dass in den letzten Jahren neuronale Netze deutlich an Popularität gewonnen haben – diese kommen in beiden, schon älteren, Standardwerken (so gut wie) nicht vor. Dafür präsentiert dieses Buch, welche Ansätze bisher in der Computerlinguistik verfolgt wurden und wie erfolgreich diese waren. Mit diesem Wissen lassen sich auch heutige computerlinguistische Artikel, die Erfolge neuronale Netze preisen, besser einordnen.This book is a fundamental tool to understand the natural language and related rules. It is the best choice we can make to understand the language and translate it to the computer.

⭐One of the finest introductory text in NLP. Deals statistical aspects of NLP in detail.

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