Unsupervised Lexical Substitution with a Word Space Model

We describe a system to tackle the Lexical Substitution task that exploits, as its only resource, co-occurrence statistics from a large PoS-tagged corpus. The system exploits the word space model formalism, and represents the word to be substituted by a composite vector that takes into account both the overall distribution of the word in the input corpus and its local context. As far as the precision and recall are concerned, the system is ranked among the highest positions in the Evalita competition, while it results winner in the mode p and mode r ranking.