Paper: Grammatical Error Detection and Correction using a Single Maximum Entropy Model

ACL ID W14-1710
Title Grammatical Error Detection and Correction using a Single Maximum Entropy Model
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2014
Authors

This paper describes the system of Shang- hai Jiao Tong Unvierity team in the CoNLL-2014 shared task. Error correc- tion operations are encoded as a group of predefined labels and therefore the task is formulized as a multi-label classifica- tion task. For training, labels are obtained through a strict rule-based approach. For decoding, errors are detected and correct- ed according to the classification results. A single maximum entropy model is used for the classification implementation in- corporated with an improved feature selec- tion algorithm. Our system achieved pre- cision of 29.83, recall of 5.16 and F 0.5 of 15.24 in the official evaluation.