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        <title>A study of N-gram and Embedding Representations for Native Language Identification</title>
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            <namePart type="given">Joel</namePart>
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    <abstract>We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.</abstract>
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%0 Conference Proceedings
%T A study of N-gram and Embedding Representations for Native Language Identification
%A Vajjala, Sowmya
%A Banerjee, Sagnik
%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 vajjala-banerjee-2017-study
%X We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.
%R 10.18653/v1/W17-5026
%U https://aclanthology.org/W17-5026/
%U https://doi.org/10.18653/v1/W17-5026
%P 240-248

Markdown (Informal)

[A study of N-gram and Embedding Representations for Native Language Identification](https://aclanthology.org/W17-5026/) (Vajjala & Banerjee, BEA 2017)

• A study of N-gram and Embedding Representations for Native Language Identification (Vajjala & Banerjee, BEA 2017)

ACL

• Sowmya Vajjala and Sagnik Banerjee. 2017. A study of N-gram and Embedding Representations for Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 240–248, Copenhagen, Denmark. Association for Computational Linguistics.