# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 4 Experimental Design and Analysis
### Comparative Experiments and Analysis
This method treats NER as a word-to-word relationship classification problem, replacing both sequence labeling and span-based methods. Among these models, BiLSTM-CRF, BERT-CRF, RBC, BIC, and RIC are based on sequence labeling methods. LB-BMBC is a span-based method, while W2 NER is a SOTA model proposing a new word-to-word relationship classification scheme. Additionally, RWGNN introduces an innovative approach by designing a unique random graph structure and enhanced word embeddings, offering new insights into NER architecture. From Table 4, it is evident that for the Resume dataset, which features relatively uniform text formats and structured content, models tend to perform well. This is because the standardized nature of the data minimizes the complexity and noise introduced by unstructured text, thereby reducing the chances of error. The WP-XAG model proposed in this paper leverages ADMHA, which allows dynamic adjustment of attention weights based on contextual information. This flexibility enables the model to capture information at different levels and granularities more effectively, leading to superior performance in comparison to other models. The BIOES tagging scheme, which clearly distinguishes between the beginning, inside, end, and singleton entities, provides finer-grained labeling information, allowing models to capture entity boundaries more accurately. This is especially relevant for datasets like Weibo, which feature colloquial language, ambiguous words, and a higher level of noise. In such cases, fine-grained labeling is crucial to reducing decoding errors. Even when compared to models like BiLSTM-CRF, BERT-CRF, RBC, BIC, and RIC, which use the BIOES scheme for more detailed labeling, WP-XAG demonstrates a distinct performance advantage. WP-XAG employs WoBERT to mitigate the effects of polysemy, providing richer semantic representations. The incorporation of adversarial training improves the model's robustness to noisy data, while the feature fusion layer further refines disambiguated semantic information. Additionally, GlobalPointer improves the model's ability to capture global information, resulting in superior performance on the Weibo dataset. In the nested entity dataset CMeEE and the fine-grained dataset CLUENER2020, capturing contextual dependencies and appropriately allocating attention becomes even more critical. For nested entities, entity boundaries are not solely determined by local information but also depend on the context of the entire sentence. In WP-XAG, the XLSTM effectively captures longer contextual dependencies, while the adaptive weights in ADMHA dynamically adjust the attention distribution across different heads, improving the model's ability to focus on various types of entities. Additionally, GlobalPointer captures the positional relationships among entities throughout the text, allowing the model to identify longer-span or more complex entity structures efficiently. The complementary strengths of these modules enable WP-XAG to achieve strong performance across datasets, further demonstrating its effectiveness in NER tasks. When compared to the SOTA model W2 NER, WP-XAG also shows performance advantages across various datasets. F1-scores improve by 0.24% on Resume, 2.66% on Weibo, 0.58% on CMeEE, and 0.45% on CLUENER2020. Furthermore, compared to other span-based models like LB-BMBC, WP-XAG demonstrates significant improvements. Compared to LB-BMBC, WP-XAG outperforms it on Resume and Weibo, with F1-score improvements of 0.60% and 6.25%, respectively. In summary, the proposed WP-XAG model for Chinese NER, based on multi-level representation learning, effectively addresses the issue of polysemy, enhances model robustness, and improves its ability to capture long-range dependencies. Its performance on Resume, Weibo, and the nested entity dataset CMeEE, as well as the fine-grained datasetCLUENER2020, surpasses that of the baseline model BiLSTM-CRF and eight other SOTA models.