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Indexed by:会议论文

Date of Publication:2005-05-30

Included Journals:EI

Volume:3497

Issue:II

Page Number:246-250

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. © Springer-Verlag Berlin Heidelberg 2005.