基于相似字特征的中文拼写纠错方法

hinese Spelling Correction (CSC) aims to accurately detect and rectify spelling errors in Chinese text. This task is pivotal for ensuring the textual content\'s accuracy and readability, representing a critical component within the field of Chinese Information Processing. Currently, the Chinese text error correction task mainly relies on the language model of BERT to perform. Among these, Soft-Masked BERT is one of the mainstream methods, which first utilizes a detection network to regress the error probability of each character at its respective position, then encodes feature vectors througChinese spelling error correction method based on similar character featuresh a soft masking mechanism, feeding them into a correction network built upon BERT. However, the original method cannot effectively handle similarities between input-output pairs and consistent symbol errors. In response to these limitations, this project proposes a Chinese character spelling correction model based on Soft-Masked BERT. In order to increase BERT\'s ability to capture similar word features, we generate a similarity graph between the signs and incorporate it into the model using graphical attention networks, which enables the similarities and differences between the revised signs and their original signs to be interpreted more effectively by the model. The method employs LSKnet units to facilitate associative learning among adjacent characters within candidate sentences, thus enhancing the overall fluency of the sentences. Experimental results demonstrate that this method has shown promising performance in the context of Chinese spelling correction tasks.