To address this problem, in recent work we presented a semi-supervised approach based on kernel methods for WSD (Strapparava, Gliozzo, and Giuliano 2004;Gliozzo, Giuliano, and Strapparava 2005;Giuliano, Gliozzo, and Strapparava 2006).
we explored the following research directions: (1) independently modeling domain and syntagmatic aspects of sense distinction to improve feature representativeness; and (2) exploiting external knowledge acquired from unlabeled data, with the purpose of drastically reducing the amount of labeled training data.