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
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-5538.pdf