Discovering Fuzzy Synsets from the Redundancy across several Dictionaries

Abstract

In a wordnet, concepts are typically represented as groups of words, commonly known as synsets, and each membership of a word to a synset denotes a different sense of that word.
However, since word senses are complex entities, without well-defined boundaries, we suggest to handle them less artificially, by representing them as fuzzy objects, where each word has its membership degree, which can be related to the confidence on using the word to denote the concept conveyed by the synset.
We thus propose an approach to discover synsets from a synonymy network, ideally redundant and extracted from several broad-coverage sou rces. The more synonymy relations there are between two words, the higher the confidence on the semantic equivalence of at least one of their senses.The proposed approach was applied to a network extracted from three Portuguese dictionaries and resulted in a large set of fuzzy synsets.
Besides describing this approach and illustrating its results, we rely on three evaluations -- comparison against a handcrafted Portuguese thesaurus; comparison against the results of a previous approach with a similar goal; and manual evaluation -- to believe that our outcomes are positive and that, in the future, they might my expanded by exploring additional synonymy sources.