Using big data to support automatic Word Sense Disambiguation

Word Sense Disambiguation (WSD) usually relies on data structures built upon the words to be disambiguated. This is a time-consuming process that requires a huge computational effort. In this paper, we propose an approach to automatically build a generic sense inventory (called iSC) to be used as a reference for disambiguation. The sense inventory is built extracting insight from Big Data exploiting a community detection algorithm. Since generate taking into account large corpora of data, the iSC is independent of the domain of application and of predefined target words.