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    <titleInfo>
        <title>Geometric Deep Learning Models for Linking Character Names in Novels</title>
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    <name type="personal">
        <namePart type="given">Marek</namePart>
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            <title>Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</title>
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        <name type="personal">
            <namePart type="given">Stefania</namePart>
            <namePart type="family">DeGaetano</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Kazantseva</namePart>
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        <name type="personal">
            <namePart type="given">Nils</namePart>
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        <name type="personal">
            <namePart type="given">Stan</namePart>
            <namePart type="family">Szpakowicz</namePart>
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    <abstract>The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.</abstract>
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%0 Conference Proceedings
%T Geometric Deep Learning Models for Linking Character Names in Novels
%A Kubis, Marek
%Y DeGaetano, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F kubis-2020-geometric
%X The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.
%U https://aclanthology.org/2020.latechclfl-1.15/
%P 127-132

Markdown (Informal)

[Geometric Deep Learning Models for Linking Character Names in Novels](https://aclanthology.org/2020.latechclfl-1.15/) (Kubis, LaTeCHCLfL 2020)

• Geometric Deep Learning Models for Linking Character Names in Novels (Kubis, LaTeCHCLfL 2020)

ACL

• Marek Kubis. 2020. Geometric Deep Learning Models for Linking Character Names in Novels. In Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 127–132, Online. International Committee on Computational Linguistics.