Using wiktionary to improve lexical disambiguation in multiple languages
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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This paper presents a semi-supervised learning method for Vietnamese part of speech tagging. We take into account two powerful tagging models including Conditional Random Fields (CRFs)and the Guided Online-Learning models (GLs) as base learning models. We then propose a semi-supervised learning tagging model for both CRFs and GLs methods. The main idea is to use of a word-cluster model as an associate source for enrich the feature space of discriminate learning models for both training and decoding processes. Experimental results on Vietnamese Tree-bank data (VTB) showed that the proposed method is effective. Our best model achieved accuracy of 94.10\% when tested on VTB, and 92.60\% an independent test.