Text local error detection and correction based on N-gram for Chinese homophone replacement
Bo Zhang, Xiaoxiao Wang, Shaopeng Yuan, Jingyang Wang, Pengfei Liu · 2022
With the continuous development of the Internet, more and more information enter people's lives. However, the information is mixed. It is difficult to guarantee the correctness of the text. For errors caused by homophone replacement, an automatic Chinese text local error detection and correction solution based on n-gram model is proposed. A method of local error detection based on the combined model of 2-gram and 3-gram is proposed, and a method of local error correction based on 3-gram model is proposed. Experiments show that the error detection recall rate is 83.1%, the error detection accuracy rate is 41.5%, the F-score is 55.4%; the error correction rate is 78.1%. The method is compared with the 2-gram model and the 3-gram model. The accuracy of error detection is increased by 7.2% and 8.2% respectively. The F-score is increased by 6.3% and 8.2% respectively.