Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry

Timo Baumann, Hussein Hussein, Burkhard Meyer-Sickendiek

Abstract
We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.
Anthology ID:
W18-4505
Volume:
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editors:
Beatrice Alex, Stefania Degaetano-Ortlieb, Anna Feldman, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
Venue:
LaTeCH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–49
Language:
URL:
https://aclanthology.org/W18-4505
DOI:
Bibkey:
Cite (ACL):
Timo Baumann, Hussein Hussein, and Burkhard Meyer-Sickendiek. 2018. Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry. In Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 44–49, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry (Baumann et al., LaTeCH 2018)
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