@inproceedings{kesarwani-etal-2017-metaphor,
title = "Metaphor Detection in a Poetry Corpus",
author = "Kesarwani, Vaibhav and
Inkpen, Diana and
Szpakowicz, Stan and
Tanasescu, Chris",
editor = "Alex, Beatrice and
Degaetano-Ortlieb, Stefania and
Feldman, Anna and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2201",
doi = "10.18653/v1/W17-2201",
pages = "1--9",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Metaphor Detection in a Poetry Corpus
%A Kesarwani, Vaibhav
%A Inkpen, Diana
%A Szpakowicz, Stan
%A Tanasescu, Chris
%Y Alex, Beatrice
%Y Degaetano-Ortlieb, Stefania
%Y Feldman, Anna
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F kesarwani-etal-2017-metaphor
%X 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.
%R 10.18653/v1/W17-2201
%U https://aclanthology.org/W17-2201
%U https://doi.org/10.18653/v1/W17-2201
%P 1-9
Markdown (Informal)
[Metaphor Detection in a Poetry Corpus](https://aclanthology.org/W17-2201) (Kesarwani et al., LaTeCH 2017)
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.