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Book
xxxi, 988 p. : ill. ; 25 cm.
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology -- at all levels and with all modern technologies -- this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.Supplements: Click on the " Resources" tab to View Downloadable Files: *Solutions (available 8/15/08)*Power Point Lecture Slides (available 8/15/08) For additional resourcse visit the author website: http://www. cs.colorado.edu/~martin/slp.html.
(source: Nielsen Book Data)9780131873216 20160528
Engineering Library (Terman)
CS-124-01, LINGUIST-180-01, LINGUIST-280-01
Book
xxi, 482 p. : ill. ; 27 cm.
  • 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.
  • (source: Nielsen Book Data)
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.
(source: Nielsen Book Data)
Engineering Library (Terman), eReserve
CS-124-01, LINGUIST-180-01, LINGUIST-280-01