# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 4 Experimental Design and Analysis
### Comparative Experiments and Analysis  
As shown in Table 4, we use bold values to represent the highest values for the corresponding metrics in the table. To verify the effectiveness of the proposed WP-XAG model in Chinese NER tasks, we conducted extensive comparative experiments using the aforementioned four public Chinese datasets. These datasets cover a wide range of domains and text types, providing a comprehensive evaluation of the model's performance and generalization capabilities across different scenarios. For comparison, we selected several models commonly used in NER due to their established performance in Chinese NER tasks. This selection allows for a robust assessment of WP-XAG's capabilities. The baseline and comparison models used are listed below:  
BiLSTM-CRF: A popular algorithm in NER, BiLSTM is used to capture contextual information and outputs the probability of each word corresponding to each label. The CRF component ensures the legality of the predicted entity label sequence, thereby improving the accuracy of entity boundary detection. BERT-CRF: Combines BERT's deep context understanding with CRF's sequence modeling for enhanced NER, offering improved accuracy and handling of complex entities. RBC: Combines the RoBERTa model to extract deep semantic features, BiLSTM to learn sequence context information, and CRF for label decoding to achieve fine-grained entity recognition. BIC: An integrated model combining BERT-wwm, IDCNN, and CRF in a composite neural network for Chinese NER. RIC: Integrates a pre-trained language model, an improved deep convolutional network IDCNN, and CRF sequence tagging technology to achieve efficient NER. RWGNN [30]: This model incorporates lexical information and uses a random graph algorithm to automatically generate multi-directional connection patterns, overcoming the limitations of manually designed graph structures. It also introduces enhanced word embeddings by combining reconstructed word information with the original word data to capture global dependencies. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Resume**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**CMeEE**} & \multicolumn{3}{c}{**CLUENER2020**} \\  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline WP-XAG & 96.94 & **96.84** & **96.89** & **72.95** & **76.94** & **74.89** & **72.87** & **71.53** & **72.19** & 80.21 & **81.73** & **80.96** \\ BiLSTM-CRF & 91.67 & 91.85 & 91.76 & 63.82 & 49.87 & 55.99 & 66.31 & 56.19 & 60.83 & 69.62 & 67.06 & 68.31 \\ BERT-CRF & 94.67 & 96.06 & 95.36 & 62.83 & 69.61 & 66.05 & 70.33 & 65.28 & 67.71 & 72.34 & 80.27 & 76.10 \\ RBC & 95.65 & 96.09 & 95.87 & 70.84 & 72.58 & 71.69 & 70.29 & 66.14 & 68.15 & 79.14 & 80.76 & 79.94 \\ BIC & 95.97 & 96.06 & 96.01 & 66.23 & 71.15 & 68.60 & 71.98 & 66.03 & 68.88 & 78.58 & 81.09 & 79.81 \\ RIC & 95.72 & 96.29 & 96.00 & 68.97 & 71.98 & 70.44 & 71.75 & 65.84 & 68.66 & 78.21 & 80.73 & 79.45 \\ RWGNN & 96.14 & 96.52 & 96.33 & 58.69 & 66.42 & 62.32 & - & - & - & - & - \\ LB-BMBC & 96.15 & 94.44 & 96.29 & 65.01 & 72.71 & 68.64 & - & - & - & - & - \\ W\({}^{2}\)NER & **96.96** & 96.35 & 96.65 & 70.84 & 73.87 & 72.32 & 72.18 & 71.04 & 71.61 & **82.16** & 78.92 & 80.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparative experiment (%). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline learning\_rate & \(2\times 10^{-5}\) \\ batch\_size & **8** \\ optimizer & Adam \\ seed & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings. LB-BMBC [22]: Leverages the word-learning capabilities of pre-trained models, observes spatial relationships between adjacent spans in span-based recognition, and uses CNN to model these relationships. W2 NER [31]: A state-of-the-art (SOTA) model that uses BERT and BiLSTM as input encoders and employs multi-granularity 2D convolution to refine word-pair representations.