Reducing Polysemy in WordNet

A known problem of WordNet is that it is too ne-grained in its sense denitions. For instance, it does not distinguish between homographs and polysemes. This distinction is crucial in many natural language processing tasks. In this paper we propose to distinguish only between homographs withinWordNet data while merging all polysemous senses. The ultimate goal of this exercise is to compute a more coarsegrained version of linguistic database. In order to achieve this task we propose to merge all polysemous senses according to similarity scores computed by a hybrid algorithm. The key idea of the algorithm is to combine the similarity scores produced by diverse semantic similarity algorithms. We implemented the algorithm and evaluated it on the dataset extracted from the WordNet. The evaluation results are promising in comparison to the other state of the art approaches.