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
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–109
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.16
DOI:
10.18653/v1/2020.figlang-1.16
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.16.pdf
Video:
 http://slideslive.com/38929723