Machine Learning for Metrical Analysis of English Poetry

Manex Agirrezabal, Iñaki Alegria, Mans Hulden

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Abstract
In this work we tackle the challenge of identifying rhythmic patterns in poetry written in English. Although poetry is a literary form that makes use standard meters usually repeated among different authors, we will see in this paper how performing such analyses is a difficult task in machine learning due to the unexpected deviations from such standard patterns. After breaking down some examples of classical poetry, we apply a number of NLP techniques for the scansion of poetry, training and testing our systems against a human-annotated corpus. With these experiments, our purpose is establish a baseline of automatic scansion of poetry using NLP tools in a straightforward manner and to raise awareness of the difficulties of this task.
Anthology ID:
C16-1074
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
772–781
Language:
URL:
https://aclanthology.org/C16-1074
DOI:
Bibkey:
Cite (ACL):
Manex Agirrezabal, Iñaki Alegria, and Mans Hulden. 2016. Machine Learning for Metrical Analysis of English Poetry. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 772–781, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Machine Learning for Metrical Analysis of English Poetry (Agirrezabal et al., COLING 2016)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C16-1074.pdf
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