NDLI: An editor labeling model for training set expansion in Web categorization

Content Provider	IEEE Xplore Digital Library
Author	Tie-Yan Liu Hao Wan Wei-Ying Ma
Copyright Year	2005
Description	Author affiliation: Microsoft Res. Asia, China (Tie-Yan Liu)
Abstract	Automatically classifying Web pages is an effective way to manage the massive information on the Web. However, our experiments show that the state-of-the-art text categorization technologies can not achieve a satisfactory classification performance in this task. The major reason is the existence of large proportion of rare categories in Web taxonomies. The failure in such categories is simply because there is not enough information to train reliable classifiers. To tackle this problem, we propose to expand the training set of the rare categories, by simulating the labeling behavior of the human editors of Web directories. Experimental results show that in such a way, we achieved significant (relatively 93%) improvement in classification accuracy, which is highly encouraging for high performance Web classification.
Sponsorship	IEEE Comput. Soc. Web Intelligence Consortium (WIC) Assoc. for Comput. Machinery (ACM)
Starting Page	165
Ending Page	171
File Size	253940
Page Count	7
File Format	PDF
ISBN	076952415X
DOI	10.1109/WI.2005.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wide area networks Asia Text categorization Taxonomy Support vector machine classification Web pages Humans Information management Labeling
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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