Word sense disambiguation using context translation

Abstract

Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we present a WSD method based on context translation. The method is based on the assumption that translation under the same context expresses similar meanings. The method treats context words consisting of translation as the pseudo training data, and then derives the meaning of ambiguous words by utilizing the knowledge from both training and pseudo training data. Experimental results show that the proposed method can significantly improve traditional WSD accuracy by 3.17%, and outperformed the best participating system in the SemEval-2007: task #5 evaluation.