@inproceedings{agirrezabal-etal-2016-machine,
title = "Machine Learning for Metrical Analysis of {E}nglish Poetry",
author = "Agirrezabal, Manex and
Alegria, I{\~n}aki and
Hulden, Mans",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1074",
pages = "772--781",
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.",
}
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%0 Conference Proceedings
%T Machine Learning for Metrical Analysis of English Poetry
%A Agirrezabal, Manex
%A Alegria, Iñaki
%A Hulden, Mans
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F agirrezabal-etal-2016-machine
%X 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.
%U https://aclanthology.org/C16-1074
%P 772-781
Markdown (Informal)
[Machine Learning for Metrical Analysis of English Poetry](https://aclanthology.org/C16-1074) (Agirrezabal et al., COLING 2016)
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.