Journal article
A Bayesian Model of Diachronic Meaning Change
Lea Frermann, Mirella Lapata
Transactions of the Association for Computational Linguistics | MIT Press - Journals | Published : 2016
DOI: 10.1162/tacl_a_00081
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 class..
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Funding Acknowledgements
We are grateful to the anonymous reviewers whose feedback helped to substantially improve the present paper. We thank Charles Sutton and Iain Murray for helpful discussions, and acknowledge the support of EPSRC through project grant EP/I037415/1