Contextualized Character Representation for Chinese Grammatical Error Diagnosis
Jianbo Zhao | Si Li | Zhiqing Lin
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Nowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.
N-gram Model for Chinese Grammatical Error Diagnosis
Jianbo Zhao | Hao Liu | Zuyi Bao | Xiaopeng Bai | Si Li | Zhiqing Lin
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)
Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis.This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors.