Introduction to Information Retrieval

A class-tested and up-to-date textbook for introductory courses on information retrieval.

Christopher D. Manning (Author), Prabhakar Raghavan (Author), Hinrich Schütze (Author)

9780521865715, Cambridge University Press

Hardback, published 7 July 2008

506 pages, 5 b/w illus. 47 tables 263 exercises
26 x 18.5 x 3.1 cm, 1.03 kg

'This book provides what Salton and Van Rijsbergen both failed to achieve … Even more important, unlike some other books in IR, the authors appear to care about making the theory as accessible as possible to the reader, on occasion including short primers to certain topics or choosing to explain difficult concepts using simplified approaches. … its coverage [is] excellent, the quality of writing high and I was surprised how much I learned from reading it. I think the online resources are impressive.' Natural Language Engineering

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.

1. Information retrieval using the Boolean model
2. The dictionary and postings lists
3. Tolerant retrieval
4. Index construction
5. Index compression
6. Scoring and term weighting
7. Vector space retrieval
8. Evaluation in information retrieval
9. Relevance feedback and query expansion
10. XML retrieval
11. Probabilistic information retrieval
12. Language models for information retrieval
13. Text classification and Naive Bayes
14. Vector space classification
15. Support vector machines and kernel functions
16. Flat clustering
17. Hierarchical clustering
18. Dimensionality reduction and latent semantic indexing
19. Web search basics
20. Web crawling and indexes
21. Link analysis.

Subject Areas: Information retrieval [UNH]