Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

TL;DR

This paper explores the use of syllabic quantity-based rhythmic features to improve Latin authorship attribution, demonstrating their effectiveness when combined with other features across multiple datasets.

Contribution

It introduces a novel approach using syllabic quantity as rhythmic features for Latin authorship attribution, showing their positive impact in classification tasks.

Findings

Rhythmic features improve authorship discrimination.

Effective across multiple datasets and machine learning methods.

Enhances traditional topic-agnostic features.

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.