  Change and its precondition, variation, are inherent in languages. Over time,
new words enter the lexicon, others become obsolete, and existing words acquire
new senses. Associating a word's correct meaning in its historical context is a
central challenge in diachronic research. Historical corpora of classical
languages, such as Ancient Greek and Latin, typically come with rich metadata,
and existing models are limited by their inability to exploit contextual
information beyond the document timestamp. While embedding-based methods
feature among the current state of the art systems, they are lacking in the
interpretative power. In contrast, Bayesian models provide explicit and
interpretable representations of semantic change phenomena. In this chapter we
build on GASC, a recent computational approach to semantic change based on a
dynamic Bayesian mixture model. In this model, the evolution of word senses
over time is based not only on distributional information of lexical nature,
but also on text genres. We provide a systematic comparison of dynamic Bayesian
mixture models for semantic change with state-of-the-art embedding-based
models. On top of providing a full description of meaning change over time, we
show that Bayesian mixture models are highly competitive approaches to detect
binary semantic change in both Ancient Greek and Latin.
