Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations

Erik-Lân Do Dinh, Hannah Wieland, Iryna Gurevych


Abstract
We encounter metaphors every day, but only a few jump out on us and make us stumble. However, little effort has been devoted to investigating more novel metaphors in comparison to general metaphor detection efforts. We attribute this gap primarily to the lack of larger datasets that distinguish between conventionalized, i.e., very common, and novel metaphors. The goal of this paper is to alleviate this situation by introducing a crowdsourced novel metaphor annotation layer for an existing metaphor corpus. Further, we analyze our corpus and investigate correlations between novelty and features that are typically used in metaphor detection, such as concreteness ratings and more semantic features like the Potential for Metaphoricity. Finally, we present a baseline approach to assess novelty in metaphors based on our annotations.
Anthology ID:
D18-1171
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1412–1424
Language:
URL:
https://aclanthology.org/D18-1171
DOI:
10.18653/v1/D18-1171
Bibkey:
Cite (ACL):
Erik-Lân Do Dinh, Hannah Wieland, and Iryna Gurevych. 2018. Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1412–1424, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations (Do Dinh et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1171.pdf
Code
 UKPLab/emnlp2018-novel-metaphors