Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution
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by
Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani
2021
Abstract
It is well known that, within the Latin production of written text, peculiar
metric schemes were followed not only in poetic compositions, but also in many
prose works. Such metric patterns were based on so-called syllabic quantity,
i.e., on the length of the involved syllables, and there is substantial
evidence suggesting that certain authors had a preference for certain metric
patterns over others. In this research we investigate the possibility to employ
syllabic quantity as a base for deriving rhythmic features for the task of
computational authorship attribution of Latin prose texts. We test the impact
of these features on the authorship attribution task when combined with other
topic-agnostic features. Our experiments, carried out on three different
datasets, using two different machine learning methods, show that rhythmic
features based on syllabic quantity are beneficial in discriminating among
Latin prose authors.
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