Identifying metaphors using fuzzy conceptual features

Abstract

Metaphor comprehension is a challenging problem which equally intrigues researchers in linguistics as well as those working in the domain of cognition. The use of psychological features such as Imageability and Concreteness has been shown to be effective in identifying metaphors. However, there is a certain degree of vagueness and blurring boundaries between the sub-concepts of these features that has hitherto been largely ignored. In this paper, we tackle this issue of vagueness by proposing a fuzzy framework for metaphor detection whereby linguistic variables are employed to express psychological features. We develop a Mamdani Model to extract fuzzy classification rules for detecting metaphors in a text. The results of experiments conducted over a dataset of nominal metaphors reveal encouraging results with an F-score of more than 79%.