Human Hedge Perception – and its Application in Fuzzy Semantic Similarity Measures

Abstract

Fuzzy Semantic Similarity Measures are algorithms that are able to compare two or more short texts that contain human perception based words and return a numeric measure of similarity of meaning between them. Such similarity is computed using a weighting, comprised of the semantic and the syntactic composition of the short text. Similarities of individual words are computed through the use of a corpus, and ontological structures based on both WordNet – a well-known lexical database of English, and on category specific fuzzy ontologies created from the derivation of Type-I or Type-II interval fuzzy sets from human perceptions of fuzzy words. Currently, linguistic hedges are not utilized in the similarity calculation within fuzzy semantic similarity measures and are ignored. This paper describes a study, which aims to capture human perceptions for linguistic hedges typically used in natural language. Twelve linguistic hedges used within natural language are selected and an experiment is conducted to capture human perceptions of the impact of hedges on fuzzy category words. A dataset of hedge sentence pairs is created and rated in terms of similarity by human participants. Excellent inter-rater correlations and inter-class correlations are established between the average human ratings and an established fuzzy semantic similarity measure.