Abstract
Prior works in metaphor detection have largely focused on crisp binary classification of textual input into ‘metaphorical’ or ‘literal’ phrases. However, the journey of a metaphor from being novel when newly created to eventually being considered dead due to the acquired familiarity with the mapping over a time span, is a continuum. This observation guides us to the idea that a metaphorical text is indeed, partially literal and partially metaphorical. In this paper, we investigate the idea of soft metaphor detection by assigning membership values to fuzzy sets representing varying degrees of metaphoricity. We use a set of conceptual features and apply a simple unsupervised technique of Fuzzy c-means to illustrate fuzzy nature of metaphors. We report our experimental results on a dataset of nominal metaphors to illustrate the concept of soft metaphor detection and demonstrate their simultaneous membership in multiple classes by visualizing overlapping clusters, metaphor and literal.
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Notes
- 1.
WordNet search (gem): https://goo.gl/ej2PUY.
- 2.
Metaphoricity: https://goo.gl/wmgjor.
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Rai, S., Chakraverty, S., Tayal, D.K., Kukreti, Y. (2017). Soft Metaphor Detection Using Fuzzy c-Means. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_38
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