Interval Probabilistic Fuzzy WordNet

TL;DR

This paper introduces Interval Probabilistic Fuzzy (IPF) sets to better model uncertainty in synsets, improving upon fuzzy synsets by incorporating interval-based membership degrees, and presents an algorithm to construct IPF synsets for English WordNet.

Contribution

It proposes a novel Interval Probabilistic Fuzzy framework for synsets, addressing limitations of fuzzy synsets and providing an algorithm for their construction using corpus and disambiguation tools.

Findings

Constructed IPF synsets for English WordNet

Demonstrated the effectiveness of IPF sets in modeling uncertainty

Provided an algorithm applicable to any language

Abstract

WordNet lexical-database groups English words into sets of synonyms called "synsets." Synsets are utilized for several applications in the field of text-mining. However, they were also open to criticism because although, in reality, not all the members of a synset represent the meaning of that synset with the same degree, in practice, they are considered as members of the synset, identically. Thus, the fuzzy version of synsets, called fuzzy-synsets (or fuzzy word-sense classes) were proposed and studied. In this study, we discuss why (type-1) fuzzy synsets (T1 F-synsets) do not properly model the membership uncertainty, and propose an upgraded version of fuzzy synsets in which membership degrees of word-senses are represented by intervals, similar to what in Interval Type 2 Fuzzy Sets (IT2 FS) and discuss that IT2 FS theoretical framework is insufficient for analysis and design of such synsets, and propose a new concept, called Interval Probabilistic Fuzzy (IPF) sets. Then we present an algorithm for constructing the IPF synsets in any language, given a corpus and a word-sense-disambiguation system. Utilizing our algorithm and the open-American-online-corpus (OANC) and UKB word-sense-disambiguation, we constructed and published the IPF synsets of WordNet for English language.