FAST: A fuzzy semantic sentence similarity measure

Abstract

A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent perception based (fuzzy) words that are commonly used in natural language. This paper presents a new sentence similarity measure that attempts to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is an ontology based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the words. Using these relationships allows for the creation of a new ontology based semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. Experiments on FAST were conducted using a new fuzzy dataset, the creation of which is described in this paper. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures.