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    Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective

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    Author
    Chen, Hsinchun
    Issue Date
    2003-05
    Submitted date
    2004-08-16
    Keywords
    Web Mining
    World Wide Web
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Information retrieval
    Machine learning
    
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    Citation
    Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective 2003-05, 54(7):621-624 Journal of the American Society for Information Science & Technology
    Publisher
    Wiley Periodicals, Inc
    Journal
    Journal of the American Society for Information Science & Technology
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105320
    Abstract
    Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information on the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.
    Type
    Journal Article (Paginated)
    Language
    en
    Collections
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