Combining Distributional Semantic Models and Sense Distribution for Effective Italian Word Sense Disambiguation

Distributional semantics approaches have proven their ability to enhance the performance of overlap-based Word Sense Disambiguation algorithms. This paper shows the application of such a technique for the Italian language, by analysing the usage of two different Distributional Semantic Models built upon ItWaC and Wikipedia corpora, in conjunction with two different functions for leveraging the sense distributions. Results of the experimental evaluation show that the proposed method outperforms both the most frequent sense baseline and other state-of-the-art systems.