Metaphor Detection via Linguistics Enhanced Siamese Network

Shenglong Zhang, Ying Liu


Abstract
In this paper we present MisNet, a novel model for word level metaphor detection. MisNet converts two linguistic rules, i.e., Metaphor Identification Procedure (MIP) and Selectional Preference Violation (SPV) into semantic matching tasks. MIP module computes the similarity between the contextual meaning and the basic meaning of a target word. SPV module perceives the incongruity between target words and their contexts. To better represent basic meanings, MisNet utilizes dictionary resources. Empirical results indicate that MisNet achieves competitive performance on several datasets.
Anthology ID:
2022.coling-1.364
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4149–4159
Language:
URL:
https://aclanthology.org/2022.coling-1.364
DOI:
Bibkey:
Cite (ACL):
Shenglong Zhang and Ying Liu. 2022. Metaphor Detection via Linguistics Enhanced Siamese Network. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4149–4159, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Metaphor Detection via Linguistics Enhanced Siamese Network (Zhang & Liu, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.364.pdf
Code
 silasthu/misnet