Word meanings change over time and an automated procedure for extracting
this information from text would be useful for historical exploratory
studies, information retrieval or question answering. We present a
dynamic Bayesian model of diachronic meaning change, which infers
temporal word representations as a set of senses and their prevalence.
Unlike previous work, we explicitly model language change as a smooth,
gradual process. We experimentally show that this modeling decision is
beneficial: our model performs competitively on meaning change detection
tasks whilst inducing discernible word senses and their development over
time. Application of our model to the SemEval-2015 temporal
classification benchmark datasets further reveals that it performs on
par with highly optimized task-specific systems.
PDF (presented at ACL 2016)