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        <title>Uncovering Probabilistic Implications in Typological Knowledge Bases</title>
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        <namePart type="given">Johannes</namePart>
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        <namePart type="given">Yova</namePart>
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    <name type="personal">
        <namePart type="given">Ryan</namePart>
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    <name type="personal">
        <namePart type="given">Isabelle</namePart>
        <namePart type="family">Augenstein</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
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    <abstract>The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1382</identifier>
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%0 Conference Proceedings
%T Uncovering Probabilistic Implications in Typological Knowledge Bases
%A Bjerva, Johannes
%A Kementchedjhieva, Yova
%A Cotterell, Ryan
%A Augenstein, Isabelle
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bjerva-etal-2019-uncovering
%X The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.
%R 10.18653/v1/P19-1382
%U https://aclanthology.org/P19-1382
%U https://doi.org/10.18653/v1/P19-1382
%P 3924-3930

Markdown (Informal)

[Uncovering Probabilistic Implications in Typological Knowledge Bases](https://aclanthology.org/P19-1382) (Bjerva et al., ACL 2019)

• Uncovering Probabilistic Implications in Typological Knowledge Bases (Bjerva et al., ACL 2019)

ACL

• Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, and Isabelle Augenstein. 2019. Uncovering Probabilistic Implications in Typological Knowledge Bases. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3924–3930, Florence, Italy. Association for Computational Linguistics.