Improving the Selection Error Recognition in a Chinese Grammar Error Detection System
Po-Lin Chen, Shih-Hung Wu, Liang-Pu Chen, Ping-Che Yang · 2016
In this paper we report how we improve our Chinese grammar error detection system. We focus on the recognition of word selection error, which is the hardest error type defined in 2015 NLP-TEA shared task CGED. Four major error types was defined in CGED shared task, including redundant word error, missing word error, word selection error and word disorder error. Based on the conditional random fields (CRF) model, our system trains a linear tagger that can be used to recognize the errors in learners' essays. In 2015 CGED shared task, we have found that our system can achieve high precision and accuracy. Our previous system used the features in the sentence only, however, it is not sufficient to recognize the word selection error. In this paper, we propose that by integrating the collocation feature into the CRF model, our system can improve the performance on the recognition of the selection errors.