# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 7 Conclusions and Future Work  
This research successfully develops the WP-XAG model for Chinese NER, aimed at addressing the challenges posed by diverse and complex text environments. The model employs multi-level representation learning, utilizing the word-based pre-trained model WoBERT to effectively manage polysemy and enhance feature extraction. Key innovations include the integration of PGD adversarial training, which significantly improves model robustness by introducing perturbations that enhance noise handling.  
The feature fusion layer incorporates XLSTM and ADMHA, enabling the model to adeptly capture intricate contextual relationships and long-range dependencies. The use of the GlobalPointer layer further facilitates the extraction of rich semantic information, allowing the model to excel across various datasets, including structured, colloquial, and nested entity formats. Experimental results demonstrate WP-XAG's superior performance compared to SOTA models, underscoring its adaptability and generalization capabilities in NER tasks.  
The scientific novelty of this work lies in its holistic approach to NER, combining advanced techniques to improve entity recognition in challenging linguistic contexts. Practically, the findings indicate significant advancements in model performance, making WP-XAG a valuable tool for applications in information extraction across diverse domains.  
The \"small-step, multi-iteration\" strategy of PGD, while effective, is not very efficient, which may limit the model's application on larger datasets. At the same time, due to the presence of a large number of proper nouns in certain datasets, relying solely on pre-trained models may not fully capture entity information in specific domains.  
Therefore, future work may consider optimizing the model's efficiency and scalability, as well as incorporating external dictionaries or vocabulary enhancement methods to improve the recognition of proper nouns and domain-specific terminology, thereby enhancing its performance in specialized fields. Additionally, existing models still face challenges when dealing with entity recognition tasks in low-resource languages.  
Conceptualization, J.D. and W.L.; Methodology, J.D. and W.L.; Software, J.D.; Validation, J.D., S.L. and X.L.; Formal analysis, S.L., X.L. and Y.S.; Resources, Z.W.; Data curation, J.D.; Writing\(-\)original draft, J.D.; Writing\(-\)review & editing, W.L. and S.L.; Visualization, X.L.; Supervision, Y.S. and Z.W.; Project administration, W.L.; Funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ningxia Higher Education Scientific Research Project (NYG2024086), the Fundamental Research Funds for the Central Universities (2022 PT_S04), and the National Natural Science Foundation of China (62066038, 61962001).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We gratefully acknowledge the support of the School of Computer Science and Engineering, North Minzu University.  
The authors declare no conflict of interest.