Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection

Marek Rei, Luana Bulat, Douwe Kiela, Ekaterina Shutova


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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1537–1546
Language:
URL:
https://www.aclweb.org/anthology/D17-1162
DOI:
10.18653/v1/D17-1162
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/D17-1162.pdf
Video:
 https://vimeo.com/238233405