Introduction to Information Retrieval

ISBN-13: 9780521865715 / Angielski / Twarda / 2008 / 506 str.

Christopher D. Manning; Prabhakar Raghavan; Hinrich Schutze

Opis

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.

Recenzje

'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.

Spis treści

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.

O autorze

Manning, Christopher D. 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. Raghavan, Prabhakar Dr Prabhakar Raghavan is Head of Yahoo! Research and a Consulting Professor of Computer Science at Stanford University. Schütze, Hinrich Dr Hinrich Schütze resides as Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart.