At present, Chinese named entity recognition model has errors in recognizing entities with nested structures, so it can not be recognized accurately. The method based on span can find nested entities, but when detecting text, it often gene-rates span without entities, and can not clearly define the span boundary, which increases the model burden. To solve this problem, this paper proposed a Chinese nested named entity recognition model based on vocabulary fusion and span boundary detection. The model used multi-word fusion method to enhance text features, and it merged multiple lexical information rela-ted to the characters in the target sentence in the designed injection module. Ant then it integrated into BERT to obtain more comprehensive context information, and provided better span representation. Secondly, it added a span boundary detection method to divide the span boundary by predicting the first and last characters of the span by perceptual classifier. Experiments on public data sets show that the model can effectively improve the recognition accuracy.

[2023-02-14] Accepted Paper /doc/2022.11.0767/v1

[2023-08-05] Printed Article /doc/2022.11.0767/v2