Machine learning methods for Chinese web page categorization

Authors:
Ji He;Ah-Hwee Tan;Chew-Lim Tan
Affiliations:
National University of Singapore, Singapore;Kent Ridge Digital Labs, Singapore;National University of Singapore, Singapore
Venue:
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Year:
2000

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Abstract

This paper reports our evaluation of k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) on Chinese web page classification. Benchmark experiments based on a Chinese web corpus showed that their predictive performance were roughly comparable although ARAM and kNN slightly outperformed SVM in small categories. In addition, inserting rules into ARAM helped to improve performance, especially for small well-defined categories.