Paper: Correction Detection and Error Type Selection as an ESL Educational Aid

ACL ID N12-1037
Title Correction Detection and Error Type Selection as an ESL Educational Aid
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

We present a classifier that discriminates be- tween types of corrections made by teachers of English in student essays. We define a set of linguistically motivated feature templates for a log-linear classification model, train this classifier on sentence pairs extracted from the Cambridge Learner Corpus, and achieve 89% accuracy improving upon a 33% base- line. Furthermore, we incorporate our classi- fier into a novel application that takes as input a set of corrected essays that have been sen- tence aligned with their originals and outputs the individual corrections classified by error type. We report the F-Score of our implemen- tation on this task.