Feeding Syntactic Versus Semantic Knowledge to a Knowledge-lean Unsupervised Word Sense Disambiguation Algorithm with an Underlying Naïve Bayes Model

Abstract

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Naïve Bayes model. It introduces dependency-based feature selection which, to our knowledge, is used for the first time in conjunction with the Naïve Bayes model acting as clustering technique. Construction of the dependency-based semantic space required for the proposed task is discussed. The resulting disambiguation method, representing an extension of the method introduced in [15], lies at the border between unsupervised and knowledge-based techniques. Syntactic knowledge provided by dependency relations (and exemplified in the case of adjectives) is hereby compared to semantic knowledge offered by the semantic network WordNet (and examined in [15]). Our conclusion is that the Naïve Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection is a reliable alternative to the WordNet-based semantic one.