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        <title>An Automatic Approach for Document-level Topic Model Evaluation</title>
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        <namePart type="given">Shraey</namePart>
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        <namePart type="given">Han</namePart>
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    <name type="personal">
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            <title>Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)</title>
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            <namePart type="given">Roger</namePart>
            <namePart type="family">Levy</namePart>
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            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic- and document-level model quality, and that basing model evaluation on topic-level analysis can be highly misleading. We propose a method for automatically predicting topic model quality based on analysis of document-level topic allocations, and provide empirical evidence for its robustness.</abstract>
    <identifier type="citekey">bhatia-etal-2017-automatic</identifier>
    <identifier type="doi">10.18653/v1/K17-1022</identifier>
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%0 Conference Proceedings
%T An Automatic Approach for Document-level Topic Model Evaluation
%A Bhatia, Shraey
%A Lau, Jey Han
%A Baldwin, Timothy
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F bhatia-etal-2017-automatic
%X Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic- and document-level model quality, and that basing model evaluation on topic-level analysis can be highly misleading. We propose a method for automatically predicting topic model quality based on analysis of document-level topic allocations, and provide empirical evidence for its robustness.
%R 10.18653/v1/K17-1022
%U https://aclanthology.org/K17-1022
%U https://doi.org/10.18653/v1/K17-1022
%P 206-215

Markdown (Informal)

[An Automatic Approach for Document-level Topic Model Evaluation](https://aclanthology.org/K17-1022) (Bhatia et al., CoNLL 2017)

• An Automatic Approach for Document-level Topic Model Evaluation (Bhatia et al., CoNLL 2017)

ACL

• Shraey Bhatia, Jey Han Lau, and Timothy Baldwin. 2017. An Automatic Approach for Document-level Topic Model Evaluation. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 206–215, Vancouver, Canada. Association for Computational Linguistics.