A cognitively motivated word sense induction algorithm

Abstract

The way in which word senses are produced and identified is of great interest to cognitive sciences as well as to various applications in natural language processing. In this paper, we present a cognitively inspired algorithm of word sense induction. The algorithm fuses the distributional and perceptual information of words. By drawing on minimal resources - word collocations and their level of concreteness/abstractness - our algorithm automatically produces for each target noun a graph that is an endomap with a maximal number of 50 nodes. This graph represents the major senses associated with the noun. Tested on a word sense disambiguation task and on psychological data, our algorithm gains significant empirical support for its efficiency.