Dictionary-Assisted Chinese Nested Named Entity Recognition

Chinese Named Entity Recognition is a challenging task, made even more difficult by the presence of nested entity structures. Previous work on Nested Named Entity Recognition focused only on exploiting internal contextual information, while ignoring the use of external information. In this paper, we propose a dictionary-assisted Chinese Nested Named Entity Recognition model, called KBCNER. Our model uses the dictionary to obtain matching words, combines characters and phrases into character-phrase pairs, and integrates them into BERT. By doing so, we can extract richer semantic information from Chinese phrases than from a single character. The use of external information from the dictionary enhances the features of our model and obtains richer semantics. To avoid constraints from specific-length enumerations, we use bi-affine structures to obtain a global view of spans. We also model local interactions between spans using a Convolutional Neural Network (CNN), taking advantage of the spatial correlation between adjacent spans. Finally, we adopt the idea of contrastive learning based on R-drop to enhance the model's robustness. Experimental results demonstrate that our model achieves excellent performance on multiple datasets. By introducing external information, we improve the performance of the model, highlighting the significance of external information for Chinese Nested Named Entity Recognition.