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        <title>Graph convolutional networks for exploring authorship hypotheses</title>
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        <namePart type="given">Tom</namePart>
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            <title>Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</title>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.</abstract>
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    <identifier type="doi">10.18653/v1/W19-2510</identifier>
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%0 Conference Proceedings
%T Graph convolutional networks for exploring authorship hypotheses
%A Lippincott, Tom
%S Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, USA
%F lippincott-2019-graph
%X This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.
%R 10.18653/v1/W19-2510
%U https://aclanthology.org/W19-2510
%U https://doi.org/10.18653/v1/W19-2510
%P 76-81

Markdown (Informal)

[Graph convolutional networks for exploring authorship hypotheses](https://aclanthology.org/W19-2510) (Lippincott, 2019)

• Graph convolutional networks for exploring authorship hypotheses (Lippincott, 2019)

ACL

• Tom Lippincott. 2019. Graph convolutional networks for exploring authorship hypotheses. In Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 76–81, Minneapolis, USA. Association for Computational Linguistics.