Chinese syntactic category disambiguation using support vector machines

Authors:
Lishuang Li;Lihua Li;Degen Huang;Heping Song
Affiliations:
Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China;Department of Mathematics-Physics, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China;Department of Mathematics-Physics, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

This paper presents a method of processing Chinese syntactic category ambiguity with support vector machines (SVMs): extracting the word itself, candidate part-of-speech (POS) tags, the pair of candidate POS tags and their probability and context information as the features of the word vector. A training set is established. The machine learning models of disambiguation based on support vector machines are obtained using polynomial kernel functions. The testing results show that this method is efficient. The paper also gives the results obtained with neural networks for comparison.