Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2022 Workshop on Figurative Language Processing |
| Pages | 137-142 |
| Number of pages | 6 |
| ISBN (electronic) | 9781959429111 |
| Publication status | Published - 2022 |
| Event | 3rd Workshop on Figurative Language Processing, FigLang 2022 - Abu Dhabi, United Arab Emirates Duration: 8 Dec 2022 → 8 Dec 2022 |
Abstract
Metaphors frame a given target domain using concepts from another, usually more concrete, source domain. Previous research in NLP has focused on the identification of metaphors and the interpretation of their meaning. In contrast, this paper studies to what extent the source domain can be predicted computationally from a metaphorical text. Given a dataset with metaphorical texts from a finite set of source domains, we propose a contrastive learning approach that ranks source domains by their likelihood of being referred to in a metaphorical text. In experiments, it achieves reasonable performance even for rare source domains, clearly outperforming a classification baseline.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Linguistics and Language
Sustainable Development Goals
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Proceedings of the 2022 Workshop on Figurative Language Processing. 2022. p. 137-142.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - Back to the Roots
T2 - 3rd Workshop on Figurative Language Processing, FigLang 2022
AU - Sengupta, Meghdut
AU - Alshomary, Milad
AU - Wachsmuth, Henning
N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Metaphors frame a given target domain using concepts from another, usually more concrete, source domain. Previous research in NLP has focused on the identification of metaphors and the interpretation of their meaning. In contrast, this paper studies to what extent the source domain can be predicted computationally from a metaphorical text. Given a dataset with metaphorical texts from a finite set of source domains, we propose a contrastive learning approach that ranks source domains by their likelihood of being referred to in a metaphorical text. In experiments, it achieves reasonable performance even for rare source domains, clearly outperforming a classification baseline.
AB - Metaphors frame a given target domain using concepts from another, usually more concrete, source domain. Previous research in NLP has focused on the identification of metaphors and the interpretation of their meaning. In contrast, this paper studies to what extent the source domain can be predicted computationally from a metaphorical text. Given a dataset with metaphorical texts from a finite set of source domains, we propose a contrastive learning approach that ranks source domains by their likelihood of being referred to in a metaphorical text. In experiments, it achieves reasonable performance even for rare source domains, clearly outperforming a classification baseline.
UR - http://www.scopus.com/inward/record.url?scp=85153300238&partnerID=8YFLogxK
M3 - Conference contribution
SP - 137
EP - 142
BT - Proceedings of the 2022 Workshop on Figurative Language Processing
Y2 - 8 December 2022 through 8 December 2022
ER -