Back to the Roots
Predicting the Source Domain of Metaphors using Contrastive Learning
- verfasst von
- Meghdut Sengupta, Milad Alshomary, Henning Wachsmuth
- 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.
- Organisationseinheit(en)
-
Fachgebiet Maschinelle Sprachverarbeitung
Institut für Künstliche Intelligenz
- Typ
- Aufsatz in Konferenzband
- Seiten
- 137-142
- Anzahl der Seiten
- 6
- Publikationsdatum
- 2022
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Sprache und Linguistik, Artificial intelligence, Angewandte Informatik, Linguistik und Sprache
- Ziele für nachhaltige Entwicklung
- SDG 4 – Qualitativ hochwertige Bildung
