Correction Detection and Error Type Selection as an ESL Educational Aid

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Correction Detection and Error Type Selection as an ESL Educational Aid

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Title: Correction Detection and Error Type Selection as an ESL Educational Aid
Author: Yamangil, Elif; Swanson, Ben

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Citation: Swanson, Ben and Elif Yamangil. 2012. Correction Detection and Error Type Selection as an ESL Educational Aid. In Proceedings North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montreal, Canada, June 3-8, 2012. Association for Computational Linguistics. 357-361.
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Abstract: We present a classifier that discriminates between 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% baseline. Furthermore, we incorporate our classifier into a novel application that takes as input a set of corrected essays that have been sentence aligned with their originals and outputs the individual corrections classified by error type. We report the F-Score of our implementation on this task.
Published Version: http://aclweb.org/anthology/N/N12/N12-1037.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12305804
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