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Abstract: This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take... (Update)
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BibTeX entry: (Update)
S. T. Dumais and H. Chen. Hierarchical classification of web content. In Proc. of the 23rd Int'l ACM Conf. on Research and Development in Information Retrieval (SIGIR), pages 256--263, Athens, Greece, August 2000. http://citeseer.ist.psu.edu/dumais00hierarchical.html More
@inproceedings{ dumais00hierarchical,
author = "Susan T. Dumais and Hao Chen",
title = "Hierarchical classification of {W}eb content",
booktitle = "Proceedings of {SIGIR}-00, 23rd {ACM} International Conference on Research and Development in Information Retrieval",
publisher = "ACM Press, New York, US",
address = "Athens, GR",
editor = "Nicholas J. Belkin and Peter Ingwersen and Mun-Kew Leong",
pages = "256--263",
year = "2000",
url = "citeseer.ist.psu.edu/dumais00hierarchical.html" }
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