An Unsupervised Approach to Chinese Word Sense Disambiguation Based on Hownet

Abstract

The research on word sense disambiguation (WSD) has great theoretical and practical significance in many fields of natural language processing (NLP). This paper presents an unsupervised approach to Chinese word sense disambiguation based on Hownet (an electronic Chinese lexical resource). In our approach, contexts that include ambiguous words are converted into vectors by means of a second-order context method, and these context vectors are then clustered by the k-means clustering algorithm. Lastly, the ambiguous words can be disambiguated after a similarity calculation process is completed. Our experiments involved extraction of terms, and an 82.62% average accuracy rate was achieved.