2022/05/14 by Silvia Corbara, Alejandro Moreo, Fabrizio Sebastiani · 1 citation
Computer Science · Psychology · #Authorship Attribution and Profiling #Natural Language Processing Techniques #Topic Modeling #Syllabic verse #Computer science #Metric (unit) #Rhythm #Task (project management) #Natural language processing #Artificial intelligence #Rhyme #Authorship attribution #Attribution #Support vector machine #Speech recognition #Linguistics #Poetry #Psychology #Art
paper · pdf · doi:10.1002/asi.24660
openalex publication_date 2022/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/19
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 , that is, 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 support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.