# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 4 Experimental Design and Analysis
### Iterative Comparison Experiments  
In this section, we conduct experiments using the Weibo and CMeEE datasets. The Weibo dataset is characterized by its frequent use of popular terms, polysemous words, and colloquial language, making it challenging for many models to effectively learn features in the early stages of training. By utilizing the Weibo dataset, we can better assess the capability of different models in handling word ambiguity and capturing complex information. The CMeEE dataset is derived from the Chinese Biomedical Language Understanding Evaluation (CBLUE) and contains a large number of nested entities, adding to its complexity. By experimenting with the CMeEE dataset, we aim to evaluate the models' performance in processing technical terms and recognizing complex entity relationships.  
As shown in Figure 5, the non-standard sentence structures and irregular formatting in the Weibo dataset make it difficult for models to capture sentence structure and dependencies, leading to consistently lower performance for the BiLSTM-CRF model. While the RBC model achieves better results, it still falls short compared to WP-XAG, possibly due to insufficient robustness in handling the noise and complexity present in the Weibo dataset. The BIC model's F1-score exhibits instability during training, with significant fluctuations, and at later stages, the F1-score even drops to zero within certain epochs. This suggests that BIC struggles with capturing and processing complex features and is particularly susceptible to the dataset's noise. In contrast, WP-XAG achieves over 60% performance by the second epoch and stabilizes around 70%, indicating that WP-XAG quickly learns effective features early in training and is more adept at handling the complex information within the Weibo dataset.  
From the results displayed in Figure 5, WP-XAG also performs exceptionally well on the CMeEE dataset compared to other models. It reaches a high F1-score early in training and maintains stability throughout, outperforming other models. The BiLSTM-CRF model struggles with complex medical terms and nested entities, resulting in a lower F1-score. Although the RBC model can achieve a high F1-score, it performs poorly in the later stages of training, suggesting that it may have learned noise and details from the training data, leading to suboptimal performance on unseen data. The BIC model also shows instability when handling complex features, indicating that it has certain limitations in model ing complex medical texts. Overall, WP-XAG demonstrates superior performance in capturing and processing complex medical information, outperforming other models across the board.  
As shown in Figure 5, although our model has achieved better results compared to other models, further efforts are needed to reach a higher F1-score (above 90%). This may require more comprehensive solutions to address the misclassification caused by complex sequences and nested entities, as well as difficulties in recognizing polysemous words in context. Additionally, when dealing with complex medical terminology, the model sometimes struggles to accurately identify proper nouns, leading to inaccuracies. By systematically analyzing these errors, we can gain insights for future improvements, such as integrating external dictionaries to enhance the recognition of proper nouns and improving the model's handling of polysemy.