Most word sense disambiguation (WSD) methods require large quantities of manually annotated training data and/or do not exploit fully the semantic relations of thesauri. We propos...
George Tsatsaronis, Michalis Vazirgiannis, Ion And...
In this paper we describe a method for performing word sense disambiguation (WSD). The method relies on unsupervised learning and exploits functional relations among words as prod...
Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. Thus, this work focu...
This paper presents a new method for Word Sense Disambiguation based on the WordNet Domains lexical resource [4]. The underlaying working hypothesis is tha...
A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automati...