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Introduction to Information Retrieval [Hardcover]

Christopher D. Manning , Prabhakar Raghavan , Hinrich Schütze
4.0 out of 5 stars  See all reviews (1 customer review)
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Book Description

July 7 2008 0521865719 978-0521865715
Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

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Frequently Bought Together

Introduction to Information Retrieval + Foundations of Statistical Natural Language Processing
Price For Both: CDN$ 152.51

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Product Description


"This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages. You'll see how MapReduce and other approaches to parallelism allow us to go beyond megabytes and to efficiently manage petabytes."
Peter Norvig, Director of Research, Google Inc.

"Introduction to Information Retrieval is a comprehensive, up-to-date, and well-written introduction to an increasingly important and rapidly growing area of computer science. Finally, there is a high-quality textbook for an area that was desperately in need of one."
Raymond J. Mooney, Professor of Computer Sciences, University of Texas at Austin

"Through compelling exposition and choice of topics, the authors vividly convey both the fundamental ideas and the rapidly expanding reach of information retrieval as a field."
Jon Kleinberg, Professor of Computer Science, Cornell University

"Highly recommended."
H.Levkowitz, Choice Magazine

"Introduction to Information Retrieval is a comprehensive, authoritative, and well-written overview of the main topics in IR. The book offers a good balance of theory and practice, and is an excellent self-contained introductory text for those new to IR."
Olga Vechtomova, Computational Linguistics

Book Description

Coherent and up -to -date, this textbook for advanced undergraduate and beginning graduate students in computer science covers all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections

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Front Cover | Copyright | Table of Contents | Excerpt | Index
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4.0 out of 5 stars Good buy Sept. 30 2009
Book is well written and has many online references and exercises.

Easy to read and allows for quick learning.
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Most Helpful Customer Reviews on (beta) 4.5 out of 5 stars  22 reviews
31 of 32 people found the following review helpful
5.0 out of 5 stars Great Stuff Aug. 22 2008
By Devabhaktuni Srikrishna - Published on
I am a big fan of the authors 1999 book on Statistical Natural Language Processing, and I and was thrilled when I found this new book online -- just search for "Information Retrieval" on Google.

In these two books, they describe the theory behind a vast toolbox which can be used to construct new tools/products for the Internet. Now I can go back to them when the need arises.

For starters, I appreciate the detailed theoretical explanations of topics that I could not find in other texts, and the references to related work are especially helpful. One of the other books I read was Information Retrieval by Grossman, which is an older book but has a more condensed style compared to this. Grossman's discussion of clustering was more high level and referenced a few more papers that I found useful. That helped increase my interest to read through these chapters in which offer greater detail.

Before I felt like I could place each topic in its appropriate context, I had to spend six months of reading both the books, playing with code and finding s/w packages, searching the research literature, reading papers and other books, and then cycling back to the books. Here's are some suggestions for things I'd like to see:

1. A set of recomended programming tools: in some books on Perl -- such as the chapter "Natural Language Tools" in pages 149-171 in "Advanced Perl Programming" by Simon Cozens (O'Reilly) -- you get a very "quick & dirty" introduction to maybe 20-30% of the concepts in these two books along with ways to implement and play around with them. Although Perl has many natural language processing tools, the Cozens book cuts to the chase, explains which are the best tools, and shows you how to use them. I think knowing such shortcuts aids in learning how to apply and improve on them. The more complex and sophisticated topics, the more likely to make it out into the real world if they are easy to play with.

2. More data/examples on what does/doesn't work with end-users: Numbers, graphs, and charts are all good stuff. I always appreciate it when the authors referenced quantitative comparisons, real-world products, and history of Internet. One of the reasons I had to consult the research literature was to broaden my understanding of quantitative comparisons between different techniques involving end-users, which were typically done in the context of complete systems studies that users could try out.

15 of 15 people found the following review helpful
5.0 out of 5 stars My new favorite book on search Feb. 6 2009
By Amazon Customer - Published on
Format:Hardcover|Verified Purchase
Managing Gigabytes used to be my favorite book on search, but it is getting quite dated as this point. This new book is by three search gurus, Chris Manning, Prabhakar Raghavan (head of Yahoo Research), and Hinrich Schutze, and the depth of their expertise shows.

This book not only describes how to build a search engine (including crawling, indexing, ranking, classification, and clustering), but also has many of the insights you can only get from lengthy experience using these techniques at large scale.

Definitely my new favorite book on search. If you work in search or just have an interest in the field, it is a great read.
9 of 9 people found the following review helpful
5.0 out of 5 stars Good for corpus linguists too Sept. 25 2010
By K. Parent - Published on
Format:Kindle Edition|Verified Purchase
I have no desire to build an internet search engine, so I'm not the target audience. However, I do work with large corpora, some of which are unindexed. When one search I programmed (in R) took 14 hours to complete (this after one attempt produced unusable results due to a bug and another crashed twelve hours in due to the power saver mode kicking in), I knew I had to find a better way.

I knew from the free sample that this book was what I was looking for. Thinking this would be a completely a new field to me, I was surprised how much I already knew. Some of it is not relevant to corpus linguists (result ranking for example), but if you're a corpus linguist and want to build an index for your corpus, I doubt you'll find a better book than this.

And the Kindle edition is done well, which is not always the case. Websites are hyperlinked and you can jump to the next or previous section with the 5-way controller.
7 of 7 people found the following review helpful
5.0 out of 5 stars nice book! Sept. 17 2008
By S. Oh - Published on
Format:Hardcover|Verified Purchase
Although i'm a newbie in information retrieval field (I'm more of a machine learning, computer vision, timeseries person),
I like the book most for the following two reasons :
(1) detailed explanation into the level of implementation in many cases (data structures//memory size etc..)
(2) good review on practice vs. theory. The authors present diverse attractive theories, and on the other hand, discusses why sometimes just simpler methods are hard to be beaten down by those more complicated methods from their experience in practice.

I like that!
4 of 4 people found the following review helpful
5.0 out of 5 stars Nice Introduction Text April 6 2012
By Siddhardha - Published on
Format:Hardcover|Verified Purchase
The company I was working for started using Elastic search (which is built on top of Lucene), so I had to dive into details of Lucene pretty deeply. Since I had no prior background in Information Retrieval field, I decided to learn the theory first and picked up this book for that purpose. This book is a nice introductory text on Information Retrieval covering a lot of ground from index construction including posting lists, tolerant retrieval, different types of queries (boolean, phrase etc), scoring, evalution of information retrieval systems, feedback mechanisms, classifcations, clustering and crawling. Overall I liked the authors presentation style in this book. The concepts are presented very clearly for the most part. With the exception of a few chapters, it's not too math heavy, so it's suited for a wider audience from that perpsective. Web crawling chapters although small are really good. This book is written such that each chapter can be covered in one lecture, so it's nice from instructor's stand point as well. This book is the text used in some schools for Information Retrieval class. You actually don't have to buy this book since it's available online for free (although the page numbers don't match exactly, so if you are taking a class and instructor refers to a certain page, it could be a different page number on the online version). I only skipped a few chapters (Chapter 18 Latent Semantic Indexing for example) but otherwise read the book from cover to cover. It took me two months to read this book but it was well worth it. When I was done, I felt like I had a good understanding of foundations of Information Retrieval field. Since then I looked into Lucene details (using Lucene in Action) and it not only made a lot more sense but actually more enjoyable. Highly recommended without any reservation.
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