基于语境与文本结构融合的中文拼写纠错方法OA北大核心CSTPCD

In Chinese Spelling Correction(CSC)tasks,there are often problems such as insufficient semantic understanding of sentences and less use of phonetic and visual information of Chinese characters.Addressing these issues,we propose a novel error correction method based on context confidence and Chinese character similarity for Chinese spelling error correction(ECS).Based on deep learning principles,this approach integrates visual similarity of Chinese characters,and phonetic similarity of Chinese characters,and a fine-tuned pre-trained BERT model,which automatically extracts sentence semantics and exploits the similarity of Chinese characters.Specifically,we fine-tune the pre-trained Chinese BERT model to adapt to downstream Chinese spelling correction tasks.Then,we use the ideographic description sequence to capture the tree structure of Chinese characters as visual information and the phonetic sequence of Chinese characters as phonetic information.Finally,combining the visual and phonetic similarity(calculated by Levenshtein distance)of Chinese characters with the fine-tuned BERT model,we achieve the completion of the correction task.Experimental results on SIGHAN benchmark datasets show that the proposed ECS method has a huge improvement in F1-score compared with the baseline model,which is 2.1％higher on the error detection level and 2.8％higher on the error correction level,verifying the applicability of the fusion of context information,visual information and phonetic information for Chinese spelling correction tasks.