Dynamic embedded topic models and change-point detection for exploring
  literary-historical hypotheses
Hale Sirin Tom Lippincott
We present a novel combination of dynamic embedded topic models and
change-point detection to explore diachronic change of lexical semantic
modality in classical and early Christian Latin. We demonstrate several methods
for finding and characterizing patterns in the output, and relating them to
traditional scholarship in Comparative Literature and Classics. This simple
approach to unsupervised models of semantic change can be applied to any
suitable corpus, and we conclude with future directions and refinements aiming
to allow noisier, less-curated materials to meet that threshold.