One of the most difficult challenges faced by non-native speakers of English is mastering the system of English articles. We trained a maximum entropy classifier to select among a/an, the, or zero article for noun phrases (NPs), based on a set of features extracted from the local context of each. When the classifier was trained on 6 million NPs, its performance on published text was about 83% c...

We investigate the accuracy development of the English article by learners of English as a second language. The study focuses on individual learners, tracking their learning trajectories through their writings in the EF-Cambridge Open Language Database (EFCAMDAT), an open access learner corpus. We draw from 17,859 writings by 1,280 learners and ask whether article accuracy in individual learner...

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 d...