A Unified Framework for Grammar Error Correction
Longkai Zhang, Houfeng Wang · 2014
In this paper we describe the PKU system for the CoNLL-2014 grammar error correction shared task.We propose a unified framework for correcting all types of errors.We use unlabeled news texts instead of large amount of human annotated texts as training data.Based on these data, a tri-gram language model is used to correct the replacement errors while two extra classification models are trained to correct errors related to determiners and prepositions.Our system achieves 25.32% in f 0.5 on the original test data and 29.10% on the revised test data.