Mao, R., Lin, C. orcid.org/0000-0003-3454-2468 and Guerin, F. (2019) End-to-end sequential metaphor identification inspired by linguistic theories. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 57th Annual Meeting of the Association for Computational Linguistics, 28 Jul - 02 Aug 2019, Florence, Italy. Association for Computational Linguistics , pp. 3888-3898. ISBN 9781950737482
Abstract
End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jan 2020 10:53 |
Last Modified: | 27 Jan 2020 10:53 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Identification Number: | 10.18653/v1/P19-1378 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155259 |