Metaphor Detection in a Poetry Corpus

Vaibhav Kesarwani, Diana Inkpen, Stan Szpakowicz, Chris Tanasescu


Abstract
Metaphor is indispensable in poetry. It showcases the poet’s creativity, and contributes to the overall emotional pertinence of the poem while honing its specific rhetorical impact. Previous work on metaphor detection relies on either rule-based or statistical models, none of them applied to poetry. Our method focuses on metaphor detection in a poetry corpus. It combines rule-based and statistical models (word embeddings) to develop a new classification system. Our system has achieved a precision of 0.759 and a recall of 0.804 in identifying one type of metaphor in poetry.
Anthology ID:
W17-2201
Volume:
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
LaTeCH
SIG:
SIGHUM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://aclanthology.org/W17-2201
DOI:
10.18653/v1/W17-2201
Bibkey:
Cite (ACL):
Vaibhav Kesarwani, Diana Inkpen, Stan Szpakowicz, and Chris Tanasescu. 2017. Metaphor Detection in a Poetry Corpus. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 1–9, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Metaphor Detection in a Poetry Corpus (Kesarwani et al., LaTeCH 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W17-2201.pdf