Automatic Classification of Article Errors in L2 Written English
نویسندگان
چکیده
This paper presents an approach to the automatic classification of article errors in non native (L2) English writing, using data cho sen from the MELD corpus that was purposely selected to contain only cases with article errors. We report on two experiments on the data: one to assess the performance of different machine learning algorithms in predicting correct article usage, and the other to determine the feasibility of using the MELD data to iden tify which linguistic properties of the noun phrase containing the article are the most salient with respect to the classification of er rors in article usage.
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