517 research outputs found
A Comparison of Feature-Based and Neural Scansion of Poetry
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.Comment: RANLP 201
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