GPLSI: Word Coarse-grained Disambiguation aided by Basic Level Concepts

We present a corpus-based supervised learning system for coarse-grained sense disambiguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts automatically obtained from WordNet. Base Level Concepts are some synsets that generalize a hyponymy sub–hierarchy, and provides an extra level of abstraction as well as relevant information about the context of a word to be disambiguated. Our experiments proved that using this type of features results on a significant improvement of precision. Our system has achieved almost 0.8 F1 (fifth place) in the coarse–grained English all-words task using a very simple set of features plus Base Level Concepts annotation.