Detecting errors in English article usage by non-native speakers
Natural Language Engineering
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Language Resources and Evaluation
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In this paper, we present an approach to the automatic identification and correction of preposition and determiner errors in non-native (L2) English writing. We show that models of use for these parts of speech can be learned with an accuracy of 70.06% and 92.15% respectively on L1 text, and present first results in an error detection task for L2 writing.