NDLI: Combining link-based and content-based methods for web document classification

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Combining link-based and content-based methods for web document classification

Content Provider	ACM Digital Library
Author	Cristo, Marco Gonçalves, Marcos André Ziviani, Nivio Moura, Edleno Calado, Pável Ribeiro-Neto, Berthier
Abstract	This paper studies how link information can be used to improve classification results for Web collections. We evaluate four different measures of subject similarity, derived from the Web link structure, and determine how accurate they are in predicting document categories. Using a Bayesian network model, we combine these measures with the results obtained by traditional content-based classifiers. Experiments on a Web directory show that best results are achieved when links from pages outside the directory are considered. Link information alone is able to obtain gains of up to 46 points in F1, when compared to a traditional content-based classifier. The combination with content-based methods can further improve the results, but too much noise may be introduced, since the text of Web pages is a much less reliable source of information. This work provides an important insight on which measures derived from links are more appropriate to compare Web documents and how these measures can be combined with content-based algorithms to improve the effectiveness of Web classification.
Starting Page	394
Ending Page	401
Page Count	8
File Format	PDF
ISBN	1581137230
DOI	10.1145/956863.956938
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link analysis Web Classification Bayesian networks
Content Type	Text
Resource Type	Article

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