Machine Learning for Metrical Analysis of English Poetry

Manex Agirrezabal, Iñaki Alegria, Mans Hulden


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
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
772–781
Language:
URL:
https://www.aclweb.org/anthology/C16-1074
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1074.pdf