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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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DS '00 Proceedings of the Third International Conference on Discovery Science
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WWW '03 Proceedings of the 12th international conference on World Wide Web
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FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
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ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
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WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 2 - Volume 2
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Information Processing and Management: an International Journal
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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Information Processing and Management: an International Journal
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ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
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Computationally efficient scoring of activity using demographics and connectivity of entities
Information Technology and Management
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Expert Systems with Applications: An International Journal
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ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
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ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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Mining competitor relationships from online news: A network-based approach
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MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Data Mining and Knowledge Discovery
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UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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International Journal of Knowledge and Web Intelligence
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ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
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AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
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IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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Journal of Artificial Intelligence Research
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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!1 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%.