Integrating sentence- and word-level error identification for disfluency correction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Bayesian synchronous tree-substitution grammar induction and its application to sentence compression
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Helping our own: text massaging for computational linguistics as a new shared task
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Blast: a tool for error analysis of machine translation output
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
A new dataset and method for automatically grading ESOL texts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Bilingual random walk models for automated grammar correction of ESL author-produced text
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
High-order sequence modeling for language learner error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
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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.