We present a lexical-based investigation into the corpus of the opera omnia of Seneca. By applying a number of statistical techniques to textual data we aim to automatically collect similar texts into closely related groups. We demonstrate
that our objective and unsupervised method is able to distinguish the texts by work and genre.
|Title of host publication||Advances in Latent Variables. Methods, Models and Applications|
|Editors||Maurizio Carpita, Eugenio Brentari, El Mostafa Qannari|
|Number of pages||13|
|Publication status||Published - 2014|
|Name||STUDIES IN THEORETICAL AND APPLIED STATISTICS|