Enhanced Hypertext Categorization Using Hyperlinks
Soumen Chakrabarti, Byron Dom, Piotr Indyk
Full Paper (PDF)

Abstract
A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain high-quality semantic clues that are lost upon a purely term-based classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented with pre-classified samples from Yahoo! and the US Patent Database. In previous work, we developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained. This classifier misclassified 36% of the patents, indicating that classifying hypertext can be more difficult than classifying text. Naively using terms in neighboring documents increased error to 38%; our hypertext classifier reduced it to 21%. Results with the Yahoo! sample were more dramatic: the text classifier showed 68% error, whereas our hypertext classifier reduced this to only 21%.

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BIBTEX

@inproceedings{DBLP:conf/sigmod/ChakrabartiDI98,
author = {Soumen Chakrabarti and
Byron Dom and
Piotr Indyk},
editor = {Laura M. Haas and
Ashutosh Tiwary},
title = {Enhanced Hypertext Categorization Using Hyperlinks},
booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
publisher = {ACM Press},
year = {1998},
isbn = {0-89791-955-5},
pages = {307-318},
crossref = {DBLP:conf/sigmod/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}


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