Paper: UM-Checker: A Hybrid System for English Grammatical Error Correction

ACL ID W13-3605
Title UM-Checker: A Hybrid System for English Grammatical Error Correction
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2013
Authors

This paper describes the NLP2CT Grammati- cal Error Detection and Correction system for the CoNLL 2013 shared task, with a focus on the errors of article or determiner (ArtOrDet), noun number (Nn), preposition (Prep), verb form (Vform) and subject-verb agreement (SVA). A hybrid model is adopted for this spe- cial task. The process starts with spell- checking as a preprocessing step to correct any possible erroneous word. We used a Maxi- mum Entropy classifier together with manual- ly rule-based filters to detect the grammatical errors in English. A language model based on the Google N-gram corpus was employed to select the best correction candidate from a confusion matrix. We also explored a graph- based label propagation approach to overcome the sparsity problem in training the...