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    <titleInfo>
        <title>Neural Networks and Spelling Features for Native Language Identification</title>
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    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Bjerva</namePart>
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    <name type="personal">
        <namePart type="given">Gintarė</namePart>
        <namePart type="family">Grigonytė</namePart>
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    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="family">Östling</namePart>
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    <name type="personal">
        <namePart type="given">Barbara</namePart>
        <namePart type="family">Plank</namePart>
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            <title>Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>We present the RUG-SU team’s submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.</abstract>
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    <identifier type="doi">10.18653/v1/W17-5025</identifier>
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%0 Conference Proceedings
%T Neural Networks and Spelling Features for Native Language Identification
%A Bjerva, Johannes
%A Grigonytė, Gintarė
%A Östling, Robert
%A Plank, Barbara
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F bjerva-etal-2017-neural
%X We present the RUG-SU team’s submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.
%R 10.18653/v1/W17-5025
%U https://aclanthology.org/W17-5025
%U https://doi.org/10.18653/v1/W17-5025
%P 235-239

Markdown (Informal)

[Neural Networks and Spelling Features for Native Language Identification](https://aclanthology.org/W17-5025) (Bjerva et al., BEA 2017)

• Neural Networks and Spelling Features for Native Language Identification (Bjerva et al., BEA 2017)

ACL

• Johannes Bjerva, Gintarė Grigonytė, Robert Östling, and Barbara Plank. 2017. Neural Networks and Spelling Features for Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 235–239, Copenhagen, Denmark. Association for Computational Linguistics.