Christopher D. Manning,1989年毕业于澳大利亚国立大学,1995年获斯坦福大学语言学博士学位,曾先后在卡内基-梅隆大学、悉尼大学教授语言学,1999年起任斯坦福大学计算机科学和语言学副教授,其主要研究方向是统计自然语言处理、信息提取与表示,以及文本理解和文本挖掘等。
Prabhakar Raghavan,毕业于印度理工学院,后获加州大学伯克利分校计算机科学博士学位,自2005年起担任Yahoo!研究中心负责人,同时也是斯坦福大学计算机科学系顾问教授。其主要研究方向是文本及Web数据挖掘、组合优化、随机算法等,此前曾任Verity公司CTO,在IBM研究院担任过管理工作。
Hinrich Schütze,斯坦福大学博士,现任斯图加特大学自然语言处理研究所理论计算语言学主任。他在美国硅谷工作过多年,曾担任过Enkata公司首席科学家。
Class-tested and coherent, this groundbreaking new textbook teaches classic web 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.
Contents
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.
Reviews
“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
stanford的IR入门书籍,cmu stanford都在用该书作为IR入门书籍,很nice。在某些章节如果你有统计的基础来看的话,会更容易些。
评分stanford的IR入门书籍,cmu stanford都在用该书作为IR入门书籍,很nice。在某些章节如果你有统计的基础来看的话,会更容易些。
评分搜素引擎入门书籍,各方面均有涉猎,严谨,通俗易懂 入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典
评分搜素引擎入门书籍,各方面均有涉猎,严谨,通俗易懂 入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典入门经典
评分stanford的IR入门书籍,cmu stanford都在用该书作为IR入门书籍,很nice。在某些章节如果你有统计的基础来看的话,会更容易些。
良心
评分玩弄搜索引擎入门必读
评分没有全部读完...估计一段时间内也不会再读,就先标记为读过吧...
评分Stanford textbook, 比较全面的入门教材,但也只限入门而已
评分上过课,第一次较为完整刷书,对搜索引擎有一个更直观的认识。
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