A Bayesian Model of Diachronic Meaning Change

Lea Frermann, Mirella Lapata

Abstract


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.

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This work is licensed under a Creative Commons Attribution 4.0 International License.