Chinese Word Sense Disambiguation Based on Word translation and Part of speech

Abstract

For vocabulary ambiguity problem in Chinese, CNN (Convolution Neural Network) is adopted to determine true meaning of ambiguous vocabulary where word, part of speech and translation around its left and right adjacent words are used. We select disambiguation window of ambiguous word which contains two adjacent lexical units and word, partofspeech and translation are extracted as disambiguation features Based on disambiguation features, convolution neural network is used to construct word sense disambiguation (WSD) classifier Training corpus in SemEval-2007: Task#5 and semantic annotation corpus in Harbin Institute of Technology are used to optimize parameters of CNN Test corpus in SemEval-2007: Task#5 is applied to test word sense disambiguation classifier Experimental results show that compared with Bayes model and BP neural network, the proposed method in this paper can make average disambiguation accuracy improve 14.94% and 6.9%

Keywords

• vocabulary ambiguity
• convolution neural network
• lexical unit
• disambiguation feature
• word sense disambiguation