Abstract
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.- Anthology ID:
- D17-1162
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1537–1546
- Language:
- URL:
- https://aclanthology.org/D17-1162
- DOI:
- 10.18653/v1/D17-1162
- Cite (ACL):
- Marek Rei, Luana Bulat, Douwe Kiela, and Ekaterina Shutova. 2017. Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1537–1546, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection (Rei et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D17-1162.pdf