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        <title>A Bayesian Model of Diachronic Meaning Change</title>
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        <namePart type="given">Lea</namePart>
        <namePart type="family">Frermann</namePart>
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        <namePart type="given">Mirella</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00081</identifier>
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        <date>2016</date>
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            <start>31</start>
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%0 Journal Article
%T A Bayesian Model of Diachronic Meaning Change
%A Frermann, Lea
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F frermann-lapata-2016-bayesian
%X 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.
%R 10.1162/tacl_a_00081
%U https://aclanthology.org/Q16-1003
%U https://doi.org/10.1162/tacl_a_00081
%P 31-45

Markdown (Informal)

[A Bayesian Model of Diachronic Meaning Change](https://aclanthology.org/Q16-1003) (Frermann & Lapata, TACL 2016)

• A Bayesian Model of Diachronic Meaning Change (Frermann & Lapata, TACL 2016)

ACL

• Lea Frermann and Mirella Lapata. 2016. A Bayesian Model of Diachronic Meaning Change. Transactions of the Association for Computational Linguistics, 4:31–45.