An Algorithm for Fuzzification of WordNets, Supported by a Mathematical Proof

TL;DR

This paper introduces an algorithm to create fuzzy versions of WordNet-like lexical databases using a corpus and WSD system, validated by a mathematical proof, enhancing semantic nuance in text mining.

Contribution

It presents a novel algorithm for fuzzifying existing WordNets based on corpus data and WSD, supported by a formal mathematical validation.

Findings

Fuzzified English WordNet (FWN) constructed and published online.

Algorithm effectively incorporates word sense ambiguity.

Mathematical proof confirms the validity of the fuzzification process.

Abstract

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.