Grammatical error detection and correction using a single maximum entropy model

Abstract

This paper describes the system of Shanghai Jiao Tong Unvierity team in the CoNLL-2014 shared task. Error correction operations are encoded as a group of predefined labels and therefore the task is formulized as a multi-label classification task. For training, labels are obtained through a strict rule-based approach. For decoding, errors are detected and corrected according to the classification results. A single maximum entropy model is used for the classification implementation incorporated with an improved feature selection algorithm. Our system achieved precision of 29.83, recall of 5.16 and F_0.5 of 15.24 in the official evaluation.