Improved Default Sense Selection forWord Sense Disambiguation
Tobias Hawker, Matthew Honnibal · 2006
Supervised word sense disambiguation has proven incredibly difficult. Despite significant effort, there has been little success at using contextual features to accurately assign the sense of a word. Instead, few systems are able to outperform the default sense baseline of selecting the highest ranked WordNet sense. In this paper, we suggest that the situation is even worse than it might first appear: the highest ranked WordNet sense is not even the best default sense classifier. We evaluate several default sense heuristics, using supersenses and SemCor frequencies to achieve significant improvements on the WordNet ranking strategy.