A Semi-supervised Learning Method for Vietnamese Part-of-Speech Tagging

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.