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Figurative, Polysemous, Conventional: Designing a Dataset of Regular Metaphor

Metaphor, a figure of speech and a cognitive device, offers a powerful way to explain one conceptual domain in terms of another. Particularly successful metaphorical mappings are conventionalized through frequent use and lose their creative quality. They become sense extensions of polysemous words. Our dataset project captures such metaphors with ten regular polysemy patterns that manifest repetitively in the meaning structures of English words. Regular metaphor, unlike its counterpart regular metonymy, has not previously received a dedicated dataset, and we intend to close this gap. The dataset under construction features naturalistic sentences extracted from a general language corpus and is manually annotated with sense labels for metaphorically extended polysemes. Its intended use is to support linguistic, cognitive, and computational investigations into patterns of meaning in polysemy, while accounting for its complexity, regularity, continuity, and heterogeneity. We see neural language models as an excellent experimental ground for such research because they are able to show both distributional (continuous) and symbolic (discrete) behavior in language processing and representation. In this paper, we reflect on how these systems tally.