  Word meaning changes over time, depending on linguistic and extra-linguistic
factors. Associating a word's correct meaning in its historical context is a
central challenge in diachronic research, and is relevant to a range of NLP
tasks, including information retrieval and semantic search in historical texts.
Bayesian models for semantic change have emerged as a powerful tool to address
this challenge, providing explicit and interpretable representations of
semantic change phenomena. However, while corpora typically come with rich
metadata, existing models are limited by their inability to exploit contextual
information (such as text genre) beyond the document time-stamp. This is
particularly critical in the case of ancient languages, where lack of data and
long diachronic span make it harder to draw a clear distinction between
polysemy (the fact that a word has several senses) and semantic change (the
process of acquiring, losing, or changing senses), and current systems perform
poorly on these languages. We develop GASC, a dynamic semantic change model
that leverages categorical metadata about the texts' genre to boost inference
and uncover the evolution of meanings in Ancient Greek corpora. In a new
evaluation framework, our model achieves improved predictive performance
compared to the state of the art.
