This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achi...
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...
In this paper, we propose a statistical approach for clustering of artMes using on-line dictionary definitions. One of the characteristics of our approach is that every sense of w...
Supervised approaches to Word Sense Disambiguation (WSD) have been shown to outperform other approaches but are hampered by reliance on labeled training examples (the data acquisi...
Polysemy is one of the most difficult problems when dealing with natural language resources. Consequently, automated ontology learning from textual sources (such as web resources) ...