Introduction to Information Retrieval

Introduction to Information Retrieval pdf epub mobi txt 電子書 下載2025

Christopher D. Manning,1989年畢業於澳大利亞國立大學,1995年獲斯坦福大學語言學博士學位,曾先後在卡內基-梅隆大學、悉尼大學教授語言學,1999年起任斯坦福大學計算機科學和語言學副教授,其主要研究方嚮是統計自然語言處理、信息提取與錶示,以及文本理解和文本挖掘等。

Prabhakar Raghavan,畢業於印度理工學院,後獲加州大學伯剋利分校計算機科學博士學位,自2005年起擔任Yahoo!研究中心負責人,同時也是斯坦福大學計算機科學係顧問教授。其主要研究方嚮是文本及Web數據挖掘、組閤優化、隨機算法等,此前曾任Verity公司CTO,在IBM研究院擔任過管理工作。

Hinrich Schütze,斯坦福大學博士,現任斯圖加特大學自然語言處理研究所理論計算語言學主任。他在美國矽榖工作過多年,曾擔任過Enkata公司首席科學傢。

出版者:Cambridge University Press
作者:Christopher D. Manning
出品人:
頁數:506
译者:
出版時間:2008-7-7
價格:USD 74.99
裝幀:Hardcover
isbn號碼:9780521865715
叢書系列:
圖書標籤:
  • 信息檢索 
  • IR 
  • 搜索引擎 
  • 計算機 
  • 機器學習 
  • 自然語言處理 
  • 人工智能 
  • 計算機科學 
  •  
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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

具體描述

讀後感

評分

作為入門書籍,還不錯。分彆介紹瞭信息檢索領域的幾個重要概念:倒排索引、檢索引擎;tf-idf權重計算技術;嚮量空間模型,信息檢索的評價,有序檢索結果的評價MAP,ROC麯綫,NDCG等等;相關反饋技術,僞相關反饋;概率檢索模型,BM25算法;基於語言建模的信息檢索模型,各種文...  

評分

搜素引擎入門書籍,各方麵均有涉獵,嚴謹,通俗易懂 入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典

評分

作為入門書籍,還不錯。分彆介紹瞭信息檢索領域的幾個重要概念:倒排索引、檢索引擎;tf-idf權重計算技術;嚮量空間模型,信息檢索的評價,有序檢索結果的評價MAP,ROC麯綫,NDCG等等;相關反饋技術,僞相關反饋;概率檢索模型,BM25算法;基於語言建模的信息檢索模型,各種文...  

評分

搜素引擎入門書籍,各方麵均有涉獵,嚴謹,通俗易懂 入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典入門經典

評分

第一次看到這本書的時候,還是在前年,當時這本書還隻是個草稿的電子版,基本上ir所涉及到的內容都有,講的也比較全麵。 要是你英文閱讀能力還好的話,推薦去讀讀這本書,肯定會對ir有一個較為全麵的瞭解的。  

用戶評價

评分

總算讀完瞭,來自斯坦福,CMU 11-741 IR博士入門必讀書目,講解思路詳細清晰,作為IR入門書很推薦,有興趣的可參考CMU 11-741的網站自我學習 http://boston.lti.cs.cmu.edu/classes/11-741/ 。 同意一些評論中說的,關於網絡的部分確實講得不多,CMU的project在machine learning方麵偏重挺大。

评分

Stanford textbook, 比較全麵的入門教材,但也隻限入門而已

评分

從導師將電子版的發給我,已有 13 個多月瞭

评分

Stanford textbook, 比較全麵的入門教材,但也隻限入門而已

评分

沒有全部讀完...估計一段時間內也不會再讀,就先標記為讀過吧...

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