In the first strategy, which we refer to as closest, we follow the conventional approach (Budanitsky and Hirst, 2006) and measure the similarity of the two words as the similarity of their closest senses, i.e.
in order to make use of the complementary information provided by our lexical knowledge base and to obtain more accurate similarity judgements, we introduce a graph vicinity factor, that combines the structural knowledge from BabelNet's semantic network and the distributional representation of sense embeddings.