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        <title>Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora</title>
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    <name type="personal">
        <namePart type="given">Stephen</namePart>
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    <name type="personal">
        <namePart type="given">Maximilian</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.</abstract>
    <identifier type="citekey">roller-etal-2018-hearst</identifier>
    <identifier type="doi">10.18653/v1/P18-2057</identifier>
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%0 Conference Proceedings
%T Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
%A Roller, Stephen
%A Kiela, Douwe
%A Nickel, Maximilian
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F roller-etal-2018-hearst
%X Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.
%R 10.18653/v1/P18-2057
%U https://aclanthology.org/P18-2057
%U https://doi.org/10.18653/v1/P18-2057
%P 358-363

Markdown (Informal)

[Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora](https://aclanthology.org/P18-2057) (Roller et al., ACL 2018)

• Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora (Roller et al., ACL 2018)

ACL

• Stephen Roller, Douwe Kiela, and Maximilian Nickel. 2018. Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 358–363, Melbourne, Australia. Association for Computational Linguistics.