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Enhanced hypertext categorization using hyperlinks (1998)
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BibTeX
@MISC{Chakrabarti98enhancedhypertext,
author = {Soumen Chakrabarti and Byron Dom and Piotr Indyk},
title = {Enhanced hypertext categorization using hyperlinks},
year = {1998}
}
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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! â and the US Patent Database2. 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%.
Keyphrases
ibm almaden link information hypertext classifier text classifier pre-classified sample yahoo sample relmation labeling technique hypertext database well-known reuters benchmark accurate classifier unstructured hypertext database new problem previous work essential component extensive text classification literature naive use contain highquality semantic clue major challenge small neighborhood profile-based routing topic taxonomy link neighborhood u patent database2 robust statistical model
