FUSE (Fuzzy Similarity Measure) - A measure for determining fuzzy short text similarity using Interval Type-2 fuzzy sets

Abstract

Measurement of the semantic and syntactic similarity of human utterances is essential in developing language that is understandable when machines engage in dialogue with users. However, human language is complex and the semantic meaning of an utterance is usually dependent on context at a given time and also based on learnt experience of the meaning of the perception based words that are used. Limited work in terms of the representation and coverage has been done on the development of fuzzy semantic similarity measures. This paper proposes a new measure known as FUSE (FUzzy Similarity mEasure) which determines similarity using expanded categories of perception based words that have been modelled using Interval Type-2 fuzzy sets. The paper describes the method of obtaining the human ratings of these words based on Mendel’s methodology and applies them within the FUSE algorithm. FUSE is then evaluated on three established datasets and is compared with two known semantic similarity algorithms. Results indicate FUSE provides higher correlations to human ratings