  Word meanings change over time, and word senses evolve, emerge or die out in
the process. For ancient languages, where the corpora are often small and
sparse, modelling such changes accurately proves challenging, and quantifying
uncertainty in sense-change estimates consequently becomes important. GASC
(Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing
generative models that have been used to analyse sense change for target words
from an ancient Greek text corpus, using unsupervised learning without the help
of any pre-training. These models represent the senses of a given target word
such as "kosmos" (meaning decoration, order or world) as distributions over
context words, and sense prevalence as a distribution over senses. The models
are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal
changes in these representations. This paper introduces EDiSC, an Embedded DiSC
model, which combines word embeddings with DiSC to provide superior model
performance. It is shown empirically that EDiSC offers improved predictive
accuracy, ground-truth recovery and uncertainty quantification, as well as
better sampling efficiency and scalability properties with MCMC methods. The
challenges of fitting these models are also discussed.
