Automatic Chinese Proofreading Based on Deep Learning

Abstract

The rapid development of the Internet enables a large number of users to generate a large amount of network text. Correcting the grammatical errors implied in the text can make the text smoother and easier to read. It is obviously unrealistic to process massive text based on manual proofreading, which makes the text automatic. Proofreading tasks have received more attention in recent years. The rise of deep learning makes the sequence-to-sequence learning method widely used in text proofreading tasks. This paper improves the existing Chinese text automatic proofreading method based on deep learning, mainly from the following aspects: This paper uses depth-based Learning to correct and correct texts, compared to the traditional n-gram method in detecting sentence fluency, detecting incorrect words, and also generating alternative words and other accuracy has been greatly improved. The experimental results show that the training of this model can converge quickly, and the training accuracy rate is as high as 90.64%, which can effectively and accurately correct Chinese text.