This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method. Given a word and its context, Lesk algorithm exploits the idea of maximum number of shared words (maximum overlaps) between the context of a word and each definition of its senses (gloss) in order to select the proper meaning. The main contribution of our approach relies on the use of a word similarity function defined on a distributional semantic space to compute the gloss-context overlap. As sense inventory we adopt BabelNet, a large multilingual semantic network built exploiting both WordNet and Wikipedia. Besides linguistic knowledge, BabelNet also represents encyclopedic concepts coming from Wikipedia. The evaluation performed on SemEval-2013 Multilingual Word Sense Disambiguation shows that our algorithm goes beyond the most frequent sense baseline and the simplified version of the Lesk algorithm. Moreover, when compared with the other participants in SemEval-2013 task, our approach is able to outperform the best system for English.
Basile et al. REF enhanced the Lesk algorithm by using word embeddings to compute the similarity between word definitions and the target context.
An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model
COLING
Computer Science