Using Conceptual Norms for Metaphor Detection

Mingyu Wan, Kathleen Ahrens, Emmanuele Chersoni, Menghan Jiang, Qi Su, Rong Xiang, Chu-Ren Huang


Abstract
This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and AllPOS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5% higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.
Anthology ID:
2020.figlang-1.16
Volume:
Proceedings of the Second Workshop on Figurative Language Processing
Month:
July
Year:
2020
Address:
Online
Editors:
Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee, Anna Feldman, Debanjan Ghosh
Venue:
Fig-Lang
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–109
Language:
URL:
https://aclanthology.org/2020.figlang-1.16
DOI:
10.18653/v1/2020.figlang-1.16
Bibkey:
Cite (ACL):
Mingyu Wan, Kathleen Ahrens, Emmanuele Chersoni, Menghan Jiang, Qi Su, Rong Xiang, and Chu-Ren Huang. 2020. Using Conceptual Norms for Metaphor Detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 104–109, Online. Association for Computational Linguistics.
Cite (Informal):
Using Conceptual Norms for Metaphor Detection (Wan et al., Fig-Lang 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.figlang-1.16.pdf
Video:
 http://slideslive.com/38929723