Modelling metaphor with attribute-based semantics
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Peer-reviewed
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Authors
Bulat, Luana
Clark, Stephen
Shutova, Ekaterina
Abstract
One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.
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Journal Title
Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 2, Short Papers
Conference Name
Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 2, Short Papers
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Volume Title
2
Publisher
Association for Computational Linguistics