Fuzzy Influence in Fuzzy Semantic Similarity Measures

Adel, Naeemah, Crockett, Keeley ORCID: https://orcid.org/0000-0003-1941-6201, Carvalho, Joao and Cross, Valerie (2021) Fuzzy Influence in Fuzzy Semantic Similarity Measures. In: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 11 July 2021 - 14 July 2021, Luxembourg.

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Official URL: https://ieeexplore.ieee.org/document/9494535

Abstract

The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures. Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions. In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing. A fuzzy influence factor is introduced into an existing measure known as FUSE. FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories. A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets. Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.

Item Type:	Conference or Workshop Item (Paper)
Published Proceedings:	Proceedings: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Peer-reviewed:	Yes
Date Deposited:	28 Sep 2021 08:59
Publisher:	IEEE
Additional Information:	"(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Divisions:	Faculties > Science and Engineering
Subject terms:	FUZZY Systems, natural language processing, computing with words
URI:	https://e-space.mmu.ac.uk/id/eprint/627778
DOI:	https://doi.org/10.1109/FUZZ45933.2021.9494535
e-ISSN	1558-4739

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