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    <titleInfo>
        <title>Using Classifier Features to Determine Language Transfer on Morphemes</title>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
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        <name type="personal">
            <namePart type="given">Silvio</namePart>
            <namePart type="given">Ricardo</namePart>
            <namePart type="family">Cordeiro</namePart>
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        <name type="personal">
            <namePart type="given">Shereen</namePart>
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        <name type="personal">
            <namePart type="given">Umashanthi</namePart>
            <namePart type="family">Pavalanathan</namePart>
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        <name type="personal">
            <namePart type="given">Kyeongmin</namePart>
            <namePart type="family">Rim</namePart>
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    <abstract>The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner’s native language background based only on the learner’s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computational task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background. We use the NLI task as a tool to uncover cross-linguistic influence on the developmental trajectory of morphemes. We perform a cross-corpus evaluation across proficiency levels to increase the reliability and validity of the linguistic features that predict the native language background. We include native English data to determine the different morpheme patterns used by native versus non-native English speakers. Furthermore, we conduct a human NLI task to determine the type and magnitude of language transfer cues used by human raters versus the classifier.</abstract>
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%0 Conference Proceedings
%T Using Classifier Features to Determine Language Transfer on Morphemes
%A Lavrentovich, Alexandra
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F lavrentovich-2018-using
%X The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner’s native language background based only on the learner’s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computational task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background. We use the NLI task as a tool to uncover cross-linguistic influence on the developmental trajectory of morphemes. We perform a cross-corpus evaluation across proficiency levels to increase the reliability and validity of the linguistic features that predict the native language background. We include native English data to determine the different morpheme patterns used by native versus non-native English speakers. Furthermore, we conduct a human NLI task to determine the type and magnitude of language transfer cues used by human raters versus the classifier.
%R 10.18653/v1/N18-4009
%U https://aclanthology.org/N18-4009
%U https://doi.org/10.18653/v1/N18-4009
%P 61-66

Markdown (Informal)

[Using Classifier Features to Determine Language Transfer on Morphemes](https://aclanthology.org/N18-4009) (Lavrentovich, NAACL 2018)

• Using Classifier Features to Determine Language Transfer on Morphemes (Lavrentovich, NAACL 2018)

ACL

• Alexandra Lavrentovich. 2018. Using Classifier Features to Determine Language Transfer on Morphemes. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 61–66, New Orleans, Louisiana, USA. Association for Computational Linguistics.