Introduction to Information Retrieval [NOOK Book]

Overview

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, ...
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Introduction to Information Retrieval

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Overview

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.
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Editorial Reviews

From the Publisher
“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

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Product Details

  • ISBN-13: 9781139637435
  • Publisher: Cambridge University Press
  • Publication date: 12/5/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Sales rank: 1325600
  • File size: 31 MB
  • Note: This product may take a few minutes to download.

Meet 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 Schütze resides as Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart,

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Table of Contents

Table of Notation

1 Boolean retrieval 1

2 The term vocabulary and postings lists 18

3 Dictionaries and tolerant retrieval 45

4 Index construction 61

5 Index compression 78

6 Scoring, term weighting, and the vector space model 100

7 Computing scores in a complete search system 124

8 Evaluation in information retrieval 139

9 Relevance feedback and query expansion 162

10 XML retrieval 178

11 Probabilistic information retrieval 201

12 Language models for information retrieval 218

13 Text classification and Naive Bayes 234

14 Vector space classification 266

15 Support vector machines and machine learning on documents 293

16 Flat clustering 321

17 Hierarchical clustering 346

18 Matrix decompositions and latent semantic indexing 369

19 Web search basics 385

20 Web crawling and indexes 405

21 Link analysis 421

Bibliography 441

Index 469

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Customer Reviews

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  • Anonymous

    Posted Sat Sep 07 00:00:00 EDT 2013

    This is a very good book, I have read the one free on the intern

    This is a very good book, I have read the one free on the internet and I wish I have a paperback one.

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