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        <title>Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments</title>
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    <name type="personal">
        <namePart type="given">Mariano</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.</abstract>
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%0 Conference Proceedings
%T Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
%A Felice, Mariano
%A Bryant, Christopher
%A Briscoe, Ted
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F felice-etal-2016-automatic
%X We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.
%U https://aclanthology.org/C16-1079
%P 825-835

Markdown (Informal)

[Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments](https://aclanthology.org/C16-1079) (Felice et al., COLING 2016)

• Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments (Felice et al., COLING 2016)

ACL

• Mariano Felice, Christopher Bryant, and Ted Briscoe. 2016. Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 825–835, Osaka, Japan. The COLING 2016 Organizing Committee.