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    <titleInfo>
        <title>Native Language Identification With Classifier Stacking and Ensembles</title>
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    <name type="personal">
        <namePart type="given">Shervin</namePart>
        <namePart type="family">Malmasi</namePart>
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    <abstract>Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.</abstract>
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    <identifier type="doi">10.1162/coli_a_00323</identifier>
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        <date>2018-09</date>
        <detail type="volume"><number>44</number></detail>
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            <start>403</start>
            <end>446</end>
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%0 Journal Article
%T Native Language Identification With Classifier Stacking and Ensembles
%A Malmasi, Shervin
%A Dras, Mark
%J Computational Linguistics
%D 2018
%8 September
%V 44
%N 3
%I MIT Press
%C Cambridge, MA
%F malmasi-dras-2018-native
%X Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.
%R 10.1162/coli_a_00323
%U https://aclanthology.org/J18-3003
%U https://doi.org/10.1162/coli_a_00323
%P 403-446

Markdown (Informal)

[Native Language Identification With Classifier Stacking and Ensembles](https://aclanthology.org/J18-3003) (Malmasi & Dras, CL 2018)

• Native Language Identification With Classifier Stacking and Ensembles (Malmasi & Dras, CL 2018)

ACL

• Shervin Malmasi and Mark Dras. 2018. Native Language Identification With Classifier Stacking and Ensembles. Computational Linguistics, 44(3):403–446.