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| Introduction to Information Retrieval | 
| Authors: Christopher D. Manning, Prabhakar Raghavan, Hinrich Schuetze
Publisher: Cambridge University Press
Category: Book
List Price: $60.00
Buy New: $40.16
You Save: $19.84 (33%)
Buy New/Used from $40.16
Avg. Customer Rating:  (4 reviews)
Sales Rank: 56926
Languages: English (Original Language), English (Unknown), English (Published)
Media: Hardcover
Number Of Items: 1
Pages: 496
Shipping Weight (lbs): 2.3
Dimensions (in): 10 x 6.9 x 1.2
ISBN: 0521865719
Dewey Decimal Number: 025.04
EAN: 9780521865715
ASIN: 0521865719
Publication Date: July 7, 2008
Availability: Usually ships in 1-2 business days
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Product Description
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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| Customer Reviews:
Very good book on IR October 18, 2008
1 out of 1 found this review helpful
Very good book on IR. Very well written and taught all practical aspects of IR and Web Search.
nice book! September 18, 2008
1 out of 1 found this review helpful
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!
An excelent buy September 2, 2008
This is my first book about information retrieval, and I think that is perfect! The book cover all the modern topics in the information retrieval field. It's very clear and really simple to understand.
Great book!
Congratulations to the authors!
Great Stuff August 22, 2008
4 out of 4 found this review helpful
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
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