Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames

Hyeju Jang, Keith Maki, Eduard Hovy, Carolyn Rosé

Abstract
In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse. We infer implicit facets of a given metaphor frame using a semi-supervised bootstrapping approach on an unlabeled corpus. Our model applies this frame facet information to metaphor detection, and achieves the state-of-the-art performance on a social media dataset when building upon other proven features in a nonlinear machine learning model. In addition, we illustrate the mechanism through which the frame and topic information enable the more accurate metaphor detection.
Anthology ID:
W17-5538
Volume:
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
Month:
August
Year:
2017
Address:
Saarbrücken, Germany
Editors:
Kristiina Jokinen, Manfred Stede, David DeVault, Annie Louis
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
320–330
Language:
URL:
https://aclanthology.org/W17-5538
DOI:
10.18653/v1/W17-5538
Bibkey:
Cite (ACL):
Hyeju Jang, Keith Maki, Eduard Hovy, and Carolyn Rosé. 2017. Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 320–330, Saarbrücken, Germany. Association for Computational Linguistics.
Cite (Informal):
Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames (Jang et al., SIGDIAL 2017)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/W17-5538.pdf
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