Discovering word senses from text using random indexing

Abstract

Random Indexing is a novel technique for dimensionality reduction while creating Word Space model from a given text. This paper explores the possible application of Random Indexing in discovering word senses from the text. The words appearing in the text are plotted onto a multi-dimensional Word Space using Random Indexing. The geometric distance between words is used as an indicative of their semantic similarity. Soft Clustering by Committee algorithm (CBC) has been used to constellate similar words. The present work shows that the Word Space model can be used effectively to determine the similarity index required for clustering. The approach does not require parsers, lexicons or any other resources which are traditionally used in sense disambiguation of words. The proposed approach has been applied to TASA corpus and encouraging results have been obtained. © 2008 Springer-Verlag Berlin Heidelberg.