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        <title>Classifier Stacking for Native Language Identification</title>
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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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            <namePart type="given">Jill</namePart>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
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            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>This paper reports our contribution (team WLZ) to the NLI Shared Task 2017 (essay track). We first extract lexical and syntactic features from the essays, perform feature weighting and selection, and train linear support vector machine (SVM) classifiers each on an individual feature type. The output of base classifiers, as probabilities for each class, are then fed into a multilayer perceptron to predict the native language of the author. We also report the performance of each feature type, as well as the best features of a type. Our system achieves an accuracy of 86.55%, which is among the best performing systems of this shared task.</abstract>
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%0 Conference Proceedings
%T Classifier Stacking for Native Language Identification
%A Li, Wen
%A Zou, Liang
%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 li-zou-2017-classifier
%X This paper reports our contribution (team WLZ) to the NLI Shared Task 2017 (essay track). We first extract lexical and syntactic features from the essays, perform feature weighting and selection, and train linear support vector machine (SVM) classifiers each on an individual feature type. The output of base classifiers, as probabilities for each class, are then fed into a multilayer perceptron to predict the native language of the author. We also report the performance of each feature type, as well as the best features of a type. Our system achieves an accuracy of 86.55%, which is among the best performing systems of this shared task.
%R 10.18653/v1/W17-5044
%U https://aclanthology.org/W17-5044
%U https://doi.org/10.18653/v1/W17-5044
%P 390-397

Markdown (Informal)

[Classifier Stacking for Native Language Identification](https://aclanthology.org/W17-5044) (Li & Zou, BEA 2017)

• Classifier Stacking for Native Language Identification (Li & Zou, BEA 2017)

ACL

• Wen Li and Liang Zou. 2017. Classifier Stacking for Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 390–397, Copenhagen, Denmark. Association for Computational Linguistics.