Aligning verb senses in two Italian lexical semantic resources

Abstract

National audienceThis work describes the evaluations of three different approaches, Lexical Match, Sense Similarity based on Personalized Page Rank, and Semantic Match based on Shallow Frame Structures, for word sense alignment of verbs between two Italian lexical-semantic resources, MultiWordNet and the Senso Comune Lexicon. The results obtained are quite satisfying with a final F1 score of 0.47 when merging together Lexical Match and Sense Similarity