Includes bibliographical references (pages 441-468) and index

Boolean retrieval -- The term vocabulary and postings lists -- Dictionaries and tolerant retrieval -- Index construction -- Index compression -- Scoring, term weighting, and the vector space model -- Computing scores in a complete search system -- Evaluation in information retrieval -- Relevance feedback and query expansion -- XML retrieval -- Probabilistic information retrieval -- Language models for information retrieval -- Text classification and Naive Bayes -- Vector space classification -- Support vector machines and machine learning on documents -- Flat clustering -- Hierarchical clustering -- Matrix decompositions and latent semantic indexing -- Web search basics -- Web crawling and indexes -- Link analysis

"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."--Publisher's description