Automated Attribution and Intertextual Analysis
This work addresses specific problems in Classical studies, such as authorship disputes and textual influences, but is incremental as it applies existing statistical and machine learning techniques to a new domain.
The paper tackled the problem of author attribution and textual analysis in Classical studies by developing novel quantitative methods, applying them to open questions like Euripides' authorship and Seneca's intertextuality, and demonstrating efficacy through software and case studies.
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.