Combining Statistical Machine Learning with Transformation Rule Learning for Vietnamese Word Sense Disambiguation

Abstract

Word Sense Disambiguation (WSD) is the task of determining the right sense of a word depending on the context it appears. Among various approaches developed for this task, statistical machine learning methods have been showing their advantages in comparison with others. However, there are some cases which cannot be solved by a general statistical model. This paper proposes a novel framework, in which we use the rules generated by transformation based learning (TBL) to improve the performance of a statistical machine learning model. This framework can be considered as a combination of a rule-based method and statistical based method. We have developed this method for the problem of Vietnamese WSD and achieved some promising results.