Automated Attribution and Intertextual Analysis

TL;DR

This paper introduces novel quantitative methods using machine learning for author attribution and intertextual analysis, demonstrating their effectiveness on classical texts and open questions in classical studies.

Contribution

It develops new computational techniques and software tailored for classical textual analysis, applying them to authorship, influence, and interpolation cases.

Findings

Successfully attributed Euripides' authorship in tested cases

Identified intertextual influences in Seneca's poetry

Detected interpolated texts in Livy's histories

Abstract

In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis. In particular, we develop techniques and software suitable for applications to Classical study, and we illustrate the efficacy of our approach in several interesting open questions in the field. We apply our numerical analysis techniques to questions of authorship attribution in the case of the Greek tragedian Euripides, to instances of intertextuality and influence in the poetry of the Roman statesman Seneca the Younger, and to cases of "interpolated" text with respect to the histories of Livy.