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We present a novel combination of dynamic embedded topic models andchange-point detection to explore diachronic change of lexical semanticmodality in classical and early Christian Latin. We demonstrate several methodsfor finding and characterizing patterns in the output, and relating them totraditional scholarship in Comparative Literature and Classics. This simpleapproach to unsupervised models of semantic change can be applied to anysuitable corpus, and we conclude with future directions and refinements aimingto allow noisier, less-curated materials to meet that threshold.

Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses