@InProceedings{agirrezabal-alegria-hulden:2016:COLING,
  author    = {Agirrezabal, Manex  and  Alegria, I\~{n}aki  and  Hulden, Mans},
  title     = {Machine Learning for Metrical Analysis of English Poetry},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  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.},
  url       = {http://aclweb.org/anthology/C16-1074}
}