Word Sense Induction Using Correlated Topic Model

Word sense induction (WSI) is the problem of automatic identification of word senses given the corpus. This paper presents a method for solving WSI problem based on the context clustering approach. The idea behind this approach is that similar contexts indicate similar meanings. Specifically, we have successfully applied Correlated Topic Model (CTM) to partition contexts of a word into clusters, each representing a sense of that word. Different from some previous systems where a single model is built for all words, in our system, each word has its own model. Experimental result on the SemEval-2010 dataset shows that CTM is a strong tool for modelling the word´s contexts. Our system has significantly better performance than all systems participated in the SemEval-2010 workshop. In comparison to the use of other topic models for WSI, our system can explore additional useful information which is the relationship between senses of a word. The prospect of using CTM for discovering the correlation between senses of multiple words is also discussed at the end of this paper.