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    <titleInfo>
        <title>Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model</title>
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    <name type="personal">
        <namePart type="given">Dominik</namePart>
        <namePart type="family">Schlechtweg</namePart>
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    <name type="personal">
        <namePart type="given">Enrique</namePart>
        <namePart type="family">Castaneda</namePart>
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    <name type="personal">
        <namePart type="given">Jonas</namePart>
        <namePart type="family">Kuhn</namePart>
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    <name type="personal">
        <namePart type="given">Sabine</namePart>
        <namePart type="family">Schulte im Walde</namePart>
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            <title>Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics</title>
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            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
            <namePart type="given">Vivi</namePart>
            <namePart type="family">Nastase</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="family">Vulić</namePart>
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    <abstract>We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences. By providing a probabilistic model of graded word meaning we aim to approach the slippery and yet widely used notion of word sense in a novel way. The proposed framework enables us to rigorously compare models of word senses with respect to their fit to the data. We perform extensive experiments and select the empirically most adequate model.</abstract>
    <identifier type="citekey">schlechtweg-etal-2021-modeling</identifier>
    <identifier type="doi">10.18653/v1/2021.starsem-1.23</identifier>
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        <date>2021-08</date>
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            <start>241</start>
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%0 Conference Proceedings
%T Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model
%A Schlechtweg, Dominik
%A Castaneda, Enrique
%A Kuhn, Jonas
%A Schulte im Walde, Sabine
%Y Ku, Lun-Wei
%Y Nastase, Vivi
%Y Vulić, Ivan
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F schlechtweg-etal-2021-modeling
%X We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences. By providing a probabilistic model of graded word meaning we aim to approach the slippery and yet widely used notion of word sense in a novel way. The proposed framework enables us to rigorously compare models of word senses with respect to their fit to the data. We perform extensive experiments and select the empirically most adequate model.
%R 10.18653/v1/2021.starsem-1.23
%U https://aclanthology.org/2021.starsem-1.23
%U https://doi.org/10.18653/v1/2021.starsem-1.23
%P 241-251

Markdown (Informal)

[Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model](https://aclanthology.org/2021.starsem-1.23) (Schlechtweg et al., *SEM 2021)

• Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model (Schlechtweg et al., *SEM 2021)

ACL

• Dominik Schlechtweg, Enrique Castaneda, Jonas Kuhn, and Sabine Schulte im Walde. 2021. Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 241–251, Online. Association for Computational Linguistics.