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    <titleInfo>
        <title>Keynote Abstract: Too soon? The limitations of AI for event data</title>
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    <name type="personal">
        <namePart type="given">Clionadh</namePart>
        <namePart type="family">Raleigh</namePart>
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        <dateIssued>2020-05</dateIssued>
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            <title>Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Erdem</namePart>
            <namePart type="family">Yörük</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Vanni</namePart>
            <namePart type="family">Zavarella</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Hristo</namePart>
            <namePart type="family">Tanev</namePart>
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        <identifier type="isbn">979-10-95546-50-4</identifier>
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    <abstract>Not all conflict datasets offer equal levels of coverage, depth, use-ability, and content. A review of the inclusion criteria, methodology, and sourcing of leading publicly available conflict datasets demonstrates that there are significant discrepancies in the output produced by ostensibly similar projects. This keynote will question the presumption of substantial overlap between datasets, and identify a number of important gaps left by deficiencies across core criteria for effective conflict data collection and analysis.</abstract>
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%0 Conference Proceedings
%T Keynote Abstract: Too soon? The limitations of AI for event data
%A Raleigh, Clionadh
%Y Hürriyetoğlu, Ali
%Y Yörük, Erdem
%Y Zavarella, Vanni
%Y Tanev, Hristo
%S Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-50-4
%G English
%F raleigh-2020-keynote
%X Not all conflict datasets offer equal levels of coverage, depth, use-ability, and content. A review of the inclusion criteria, methodology, and sourcing of leading publicly available conflict datasets demonstrates that there are significant discrepancies in the output produced by ostensibly similar projects. This keynote will question the presumption of substantial overlap between datasets, and identify a number of important gaps left by deficiencies across core criteria for effective conflict data collection and analysis.
%U https://aclanthology.org/2020.aespen-1.2
%P 7

Markdown (Informal)

[Keynote Abstract: Too soon? The limitations of AI for event data](https://aclanthology.org/2020.aespen-1.2) (Raleigh, AESPEN 2020)

• Keynote Abstract: Too soon? The limitations of AI for event data (Raleigh, AESPEN 2020)

ACL

• Clionadh Raleigh. 2020. Keynote Abstract: Too soon? The limitations of AI for event data. In Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020, page 7, Marseille, France. European Language Resources Association (ELRA).