Metre as a stylometric feature in Latin hexameter poetry
This provides a method for stylometric analysis in classical literature, enabling authorship attribution with minimal text, but it is incremental as it applies existing machine learning models to a new feature in a specific domain.
This paper tackled the problem of identifying authorial style in classical Latin hexameter poetry by using metre as a feature, achieving at least 95% accuracy in pairwise classification with samples as small as 10-80 lines, which is much smaller than typical bag-of-words approaches.
This paper demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, pairwise classification experiments are performed between 5 first-century authors (10 comparisons) using four different machine-learning models. The results showed a two-label classification accuracy of at least 95% with samples as small as ten lines and no greater than eighty lines (up to around 500 words). These sample sizes are an order of magnitude smaller than those typically recommended for BOW ('bag of words') or n-gram approaches, and the reported accuracy is outstanding. Additionally, this paper explores the potential for novelty (forgery) detection, or 'one-class classification'. An analysis of the disputed Aldine Additamentum (Sil. Ital. Puni. 8:144-225) concludes (p=0.0013) that the metrical style differs significantly from that of the rest of the poem.