# Chinese Named Entity Recognition Based on Multi-Level Representation Learning
## 4 Experimental Design and Analysis
### Ablation Study  
As shown in Table 5, an ablation study was conducted to further verify the significance of each module in the WP-XAG model and its contribution to performance improvement. The study used a stepwise removal strategy across the four publicly available datasets used in the previous experiments. This approach helps to explore how each module affects the model's performance across different types of datasets. We use bold values to represent the highest values for the corresponding metrics in the table. The details of the ablation study are as follows:  
* PGD: The PGD applied to the embedding layer is removed. * XLSTM: The XLSTM network in the WP-XAG model is removed. * ADMHA: The ADMHA mechanism is removed. * RoPE: The RoPE in GlobalPointer is removed. From the results of the ablation experiments shown in Table 5, we can observe the following:  
After removing the PGD module, the F1-score on the Resume dataset decreased by 1.42%, indicating that PGD adversarial training enhances model robustness even in the more structured text of the Resume dataset. On the Weibo dataset, the F1-score dropped by 3.66%, which can be attributed to the more colloquial and polysemous nature of Weibo texts. PGD helps the model adapt to these polysemous words and irregular language structures, and the significant drop in F1 after its removal demonstrates PGD's effectiveness in handling noise and complex semantics. For the CMEEE dataset, the F1-score decreased by 1.37%, suggesting that while nested entities in medical texts are complex, PGD still improves the model's handling of noise and challenging annotations. On the CLUENER2020 dataset, the F1-score dropped by 3.98%, illustrating that PGD contributes significantly to the recognition of complex entities in fine-grained NER tasks. When the XLSTM module was removed, the F1-score on the Resume dataset dropped by 1.62%, showing that XLSTM contributes to capturing global semantics and long-range dependencies. On the Weibo dataset, the F1-score fell by 3.82%. Weibo contains not only complex contexts, but also many polysemous words, and XLSTM's enhanced memory structure helps the model better distinguish between different meanings of the same word in such intricate contexts. The removal of XLSTM weakened the model's ability to handle these complex dependencies and polysemy. On the CMEEE dataset, the F1-score dropped by 1.88%, indicating that XLSTM aids in the modeling of nested entities. The F1-score on CLUENER2020 decreased by 4.62%, demonstrating that XLSTM's multi-layer sequence modeling capability plays an important role in capturing subtle variations in fine-grained datasets. After removing the ADMHA module, the F1-score on the Resume dataset dropped by 1.67%. ADMHA helps capture semantic information in more structured texts. On the Weibo dataset, the F1-score decreased by 3.39%, with ADMHA playing a relatively im \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- & **96.94** & **96.84** & **96.89** & **72.95** & **76.94** & **74.89** & **72.87** & 71.53 & **72.19** & 80.21 & **81.73** & **80.96** \\ XAG(ours) & **96.94** & **96.84** & **96.89** & **72.95** & **76.94** & **74.89** & **72.87** & 71.53 & **72.19** & 80.21 & **81.73** & **80.96** \\ -PGD & 95.96 & 95.56 & 95.47 & 70.31 & 76.20 & 71.23 & 72.81 & 70.12 & 70.82 & 76.51 & 78.91 & 76.98 \\ -XLSTM & 95.63 & 95.53 & 95.27 & 72.03 & 74.09 & 71.07 & 70.40 & 71.72 & 70.31 & **82.42** & 72.72 & 76.34 \\ -ADMHA & 94.93 & 96.23 & 95.22 & 71.51 & 74.08 & 71.50 & 70.43 & **71.82** & 70.54 & 78.43 & 78.97 & 77.99 \\ -RoPE & 94.08 & 96.31 & 94.88 & 71.41 & 68.65 & 68.70 & 70.94 & 69.84 & 69.83 & 75.85 & 78.94 & 76.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ablation experiments (%).