A Comparison of Feature-Based and Neural Scansion of Poetry

Manex Agirrezabal, Iñaki Alegria, Mans Hulden


Abstract
Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.
Anthology ID:
R17-1003
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
18–23
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_003
DOI:
10.26615/978-954-452-049-6_003
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PDF:
https://doi.org/10.26615/978-954-452-049-6_003