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2017 | OriginalPaper | Buchkapitel

Identifying Metaphors Using Fuzzy Conceptual Features

verfasst von : Sunny Rai, Shampa Chakraverty, Devendra K. Tayal

Erschienen in: Information, Communication and Computing Technology

Verlag: Springer Singapore

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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%.

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Vorheriges Kapitel An Optimal Multi-view Ensemble Learning for High Dimensional Data Classification Using Constrained Particle Swarm Optimization

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Titel: Identifying Metaphors Using Fuzzy Conceptual Features
verfasst von: Sunny Rai
Shampa Chakraverty
Devendra K. Tayal
Verlag: Springer Singapore
Buch: Information, Communication and Computing Technology
Print ISBN: 978-981-10-6543-9

Electronic ISBN: 978-981-10-6544-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-6544-6_34

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