  WordNet-like Lexical Databases (WLDs) group English words into sets of
synonyms called "synsets." Although the standard WLDs are being used in many
successful Text-Mining applications, they have the limitation that word-senses
are considered to represent the meaning associated to their corresponding
synsets, to the same degree, which is not generally true. In order to overcome
this limitation, several fuzzy versions of synsets have been proposed. A common
trait of these studies is that, to the best of our knowledge, they do not aim
to produce fuzzified versions of the existing WLD's, but build new WLDs from
scratch, which has limited the attention received from the Text-Mining
community, many of whose resources and applications are based on the existing
WLDs. In this study, we present an algorithm for constructing fuzzy versions of
WLDs of any language, given a corpus of documents and a word-sense
disambiguation (WSD) system for that language. Then, using the
Open-American-National-Corpus and UKB WSD as algorithm inputs, we construct and
publish online the fuzzified version of English WordNet (FWN). We also propose
a theoretical (mathematical) proof of the validity of its results.

Artificial Intelligence