Probabilistic Latent Semantic Analysis for Unsupervised Word Sense Disambiguation

Abstract

This paper presents an unsupervised approach for disambiguating between various senses of a word to select the most appropriate sense, based on the context in the text. We have defined a Probabilistic Latent Semantic Analysis (PLSA) based Word Sense Disambiguation (WSD) system in which sense tagged annotated data is not required for training and the system is language independent giving 83% and 74% accuracy for English and Hindi languages respectively. Also, through word sense disambiguation experiments, we have shown that byapplying Word net in this algorithm, performance of our system can be further enhanced.