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.

Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

“…In this section, we describe a simple question and answer dialogue system that utilises the FUSE semantic similarity measure [17], to match user utterances to different categories of responses to each question. The dialog structure is therefore a linear sequence of questions, where each question response has three possible branches.…”

“…The aim is to distinguish between human perceptions of fuzzy words in nine categories to assess if the correct rule fires in response to natural language used within the human utterance. FUSE [17] is an ontology based similarity measure that uses Interval Type-2 fuzzy sets to model relationships between categories of human perception based words. The FUSE algorithm identifies fuzzy words in a human utterance and determines their similarity in context of both the semantic and syntactic construction of the sentence.…”

“…These categories are Size/Distance, Age, Temperature, Worth, Level of Membership, Frequency, Brightness, Strength and Speed. Initial selection and methodology for word population can be found in [17]. An experiment originally described in [17] was used to capture human ratings to create the fuzzy ontology for these categories where words were modelled based on Mendel's Hao-Mendel Approach (HMA) using Interval Type-2 fuzzy sets [24].…”

“…Initial selection and methodology for word population can be found in [17]. An experiment originally described in [17] was used to capture human ratings to create the fuzzy ontology for these categories where words were modelled based on Mendel's Hao-Mendel Approach (HMA) using Interval Type-2 fuzzy sets [24]. A full description of the FUSE algorithm and the general approach on how the fuzzy word models and measures in each category were derived is given in [17].…”

“…Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can compare two or more short texts or phrases which contain human perception-based words, and will return a numeric measure of similarity (composed of both semantic and syntactic elements) of meaning between them. This paper utilises one such measure known as FUSE (FUzzy Similarity mEasure) [17] which uses both WordNet [18] and a series of fuzzy ontologies which have been modelled from human representations using Interval Type-2 fuzzy sets [17]. FUSE has been shown to model intra-personal and inter-personal uncertainties of fuzzy words representative of natural language.…”