UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense Disambiguation

This work introduces a new unsupervised approach to multilingual word sense disambiguation. Its main purpose is to automatically choose the intended sense (meaning) of a word in a particular context for different languages. It does so by selecting the correct Babel synset for the word and the various Wiki Page titles that mention the word. BabelNet contains all the output information that our system needs, in its Babel synset. Through Babel synset, we find all the possible Synsets for the word in WordNet. Using these Synsets, we apply the disambiguation method Ppr+Freq to find what we need. To facilitate the work with WordNet, we use the ISR-WN which offers the integration of different resources to WordNet. Our system, recognized as the best in the competition, obtains results around 69% of Recall.