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        <title>How Document Pre-processing affects Keyphrase Extraction Performance</title>
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    <name type="personal">
        <namePart type="given">Florian</namePart>
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        <namePart type="given">Hugo</namePart>
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        <namePart type="given">Damien</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
            <namePart type="family">Baldwin</namePart>
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    <abstract>The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.</abstract>
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%0 Conference Proceedings
%T How Document Pre-processing affects Keyphrase Extraction Performance
%A Boudin, Florian
%A Mougard, Hugo
%A Cram, Damien
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F boudin-etal-2016-document
%X The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.
%U https://aclanthology.org/W16-3917/
%P 121-128

Markdown (Informal)

[How Document Pre-processing affects Keyphrase Extraction Performance](https://aclanthology.org/W16-3917/) (Boudin et al., WNUT 2016)

• How Document Pre-processing affects Keyphrase Extraction Performance (Boudin et al., WNUT 2016)

ACL

• Florian Boudin, Hugo Mougard, and Damien Cram. 2016. How Document Pre-processing affects Keyphrase Extraction Performance. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee.