# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 5 Conclusions and Future Work  
In real-life scenarios, text context can be complex, and texts such as medical information and daily expression often contain a large number of nested entities. The task of nested entity recognition in complex text is an important challenge. Improving the model's ability to recognize nested entities can enhance the processing speed of organizations, such as medical institutions, in handling large volumes of textual data. Enhancements to nested entity recognition models are also crucial for the development of knowledge graphs. The identified nested entities and entity categories can serve as knowledge for downstream tasks such as knowledge fusion and reasoning within the knowledge graph. Improving the entity recognition capability in each knowledge graph task can reduce error propagation from the entity recognition stage and improve the performance of each downstream task.  
Currently, there is relatively limited research on nested entity recognition in Chinese, with most Chinese entity recognition studies focusing on flat entity recognition tasks. General flat entity recognition models, such as sequence labeling-based models, cannot handle the recognition of nested entities in text. To address this issue, our model adopts the GlobalPointer module to handle potential entity boundaries and extract nested entities from the text. Classical entity recognition models lack sufficient feature extraction capabilities to capture deep semantic relationships between nested entities. To tackle this problem, our model incorporates the BERT model as the word embedding layer to extract word-level semantic information. It also employs a combination of the multi-position multi-head attention mechanism and the semantic extraction module, IDCNNLR, to extract more semanticinformation from the text. Experimental results demonstrate that the GlobalPointer module exhibits certain effectiveness in handling nested entities. With a combination of BERT layers, attention mechanisms, and IDCNNLR modules, our model can capture deeper semantic information between nested entities, thereby improving recognition performance. In summary, the future work of Chinese nested entity recognition is prospected as follows in this chapter.  
In the future, further improvements to the IDCNNLR module can be made by incorporating more complex network structures. Stronger semantic extraction modules enable the model to capture deeper semantic features, thereby enhancing overall performance. The optimization of the GlobalPointer module can be explored to improve the model's ability to compute entity boundaries.  
Conceptualization, J.L.; Methods, J.L. and W.L.; Verification, J.L., Y.G. and J.G.; Formal analysis, X.Z.; Resources, J.L.; Data Management, Y.G.; Writing\(-\)Original manuscript preparation, J.L.; Writing\(-\)Review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ningxia Natural Science Foundation Project (2021 AAC03215), the Basic Scientific Research in Central Universities of North Minzu University (2021 JCYJ12), the National Natural Science Foundation of China (62066038, 61962001).  
The data presented in this study are available on request from the corresponding author. Because the data sets in this article were developed by individual institutions, the data are not publicly available.  
The authors declare no conflict of interest.