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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C16-1074.pdf