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Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. 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. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
- Sales Rank: #652635 in eBooks
- Published on: 2008-07-07
- Released on: 2008-07-07
- Format: Kindle eBook
Review
"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
About the Author
Christopher Manning is an Associate Professor of Computer Science and Linguistics at Stanford University. His research concentrates on probabilistic models of language and statistical natural language processing, information extraction, text understanding and text mining.
Dr Prabhakar Raghavan is Head of Yahoo! Research and a Consulting Professor of Computer Science at Stanford University.
Dr Hinrich Sch�tze resides as Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart.
Most helpful customer reviews
36 of 37 people found the following review helpful.
Great Stuff
By Devabhaktuni Srikrishna
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.
Thanks,
-Sri
17 of 17 people found the following review helpful.
My new favorite book on search
By Amazon Customer
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.
12 of 12 people found the following review helpful.
Nice Introduction Text
By Siddhardha
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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