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
第一次看到这本书的时候,还是在前年,当时这本书还只是个草稿的电子版,基本上ir所涉及到的内容都有,讲的也比较全面。 要是你英文阅读能力还好的话,推荐去读读这本书,肯定会对ir有一个较为全面的了解的。
评分这本书不错。值得一看。 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...
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。
评分对于搜索引擎的初学者里说,本书是一本绝对值得阅读的书目。作者从最简单的布尔检索到一个完整的搜索引擎,逐步深入,逐步引导读者思考,对建造一个大型搜索引擎需要用到的架构和算法都有所涉猎,看完后会对搜索引擎有一个大概的认识,对其基本原理也会有所了解。搜索引擎并不...
评分最重要的收获,是对信息检索系统(搜索引擎)有一个宏观的认识,大体上说,需要从两个维度来看: 第一个是查询维度,它的核心,是两个索引结构;其一是字典,其二是倒排拉链和正排索引; 字典的职责,是把 query 变成 term set;期间用到了多种技术,如:语义扩展(同义词、拼...
Aran同学没想到你也有今天啊呵呵呵呵厚T T……
评分总算读完了,来自斯坦福,CMU 11-741 IR博士入门必读书目,讲解思路详细清晰,作为IR入门书很推荐,有兴趣的可参考CMU 11-741的网站自我学习 http://boston.lti.cs.cmu.edu/classes/11-741/ 。 同意一些评论中说的,关于网络的部分确实讲得不多,CMU的project在machine learning方面偏重挺大。
评分玩弄搜索引擎入门必读
评分好书,全面易懂,每章结尾的reference&further reading尤其好。
评分总算读完了,来自斯坦福,CMU 11-741 IR博士入门必读书目,讲解思路详细清晰,作为IR入门书很推荐,有兴趣的可参考CMU 11-741的网站自我学习 http://boston.lti.cs.cmu.edu/classes/11-741/ 。 同意一些评论中说的,关于网络的部分确实讲得不多,CMU的project在machine learning方面偏重挺大。
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