## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ACE2004**} & \multicolumn{3}{c}{**ACE2005**} & \multicolumn{3}{c}{**GENIA**} \\ \cline{2-10}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline [PERSON] et al. [42] & 81.70 & 77.40 & 79.50 & 79.00 & 77.30 & 78.20 & – & – & – \\ [PERSON] et al. [43] & – & – & 84.70 & – & – & 82.90 & – & – & 72.60 \\ [PERSON] et al. [44] & – & – & 84.33 & – & – & 83.42 & – & – & 78.20 \\ [PERSON] et al. [33] & 85.80 & 84.80 & 85.30 & 83.80 & 83.90 & 83.90 & 79.20 & 77.40 & 78.30 \\ [PERSON] et al. [45] & 86.08 & 86.48 & 86.20 & 83.95 & 85.93 & 84.60 & 79.45 & 78.94 & 79.10 \\ [PERSON] et al. [13] & 86.70 & 86.50 & 86.60 & 84.50 & 86.40 & 85.40 & 78.20 & 78.20 & 78.20 \\ [PERSON] et al. [35] & 86.90 & 85.80 & 86.30 & 85.70 & 85.20 & 85.40 & 80.30 & 78.90 & 79.60 \\ [PERSON] et al. [34] & 84.88 & 85.78 & 85.33 & 84.23 & 86.15 & 85.18 & 80.62 & 80.68 & 80.65 \\ \hline Ours & 86.96 & 86.36 & **86.66** & 84.94 & 86.73 & **85.83** & 82.35 & 80.33 & **81.33** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of our model with the baseline model on the nested entity recognition dataset. The highest score is marked in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**MSRA**} & \multicolumn{3}{c}{**Resume**} \\ \cline{2-7}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline [PERSON] and [PERSON] [41] & 93.57 & 92.79 & 93.18 & 94.81 & 94.11 & 94.46 \\ [PERSON] et al. [46] & – & – & 92.74 & – & – & 95.00 \\ [PERSON] et al. [47] & 94.50 & 92.93 & 93.71 & 95.37 & 94.84 & 95.11 \\ [PERSON] et al. [48] & 93.51 & 92.51 & 93.01 & 94.69 & 95.21 & 94.95 \\ [PERSON] et al. [49] & 94.92 & 94.19 & 94.55 & 95.63 & 95.52 & 95.58 \\ \hline Ours & 95.83 & 95.76 & **95.79** & 96.62 & 96.32 & **96.47** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of our model and the baseline model on the flat entity recognition dataset. The highest score is marked in bold. In addition, we also conducted ablation experiments on the flat entity recognition dataset Resume, and the results are shown in Table 5. The CBAM-RoPE-BAM model improved the F1 values on several entity categories. And the precision was significantly enhanced on several entity types, with 3.3% and 2.72% improvement on the TITLE and ORG entity types, respectively. However, the recall of the RoPE-BAM model dropped dramatically by only 50% on the LOC entity type, which we believe is because the number of LOC entities in the training set is too small. The average length is basically 5, but only half of the entities in the test set are of length 5, resulting in overfitting. The results of the ablation experiments on all datasets show that our proposed method is effective for nested NER, flat NER, Chinese NER, and English NER tasks, demonstrating the generalization performance of the CBAM-RoPE-BAM model. We explored the effect of the number of convolutional modules on the results of the GENIA dataset, and the results are shown in Figure 3, where N denotes the number of convolution modules. It can be seen from the table that the best performance is achieved when the number of convolutions N = 5, and the performance when only one convolution module is used is also better than that without a convolution module, demonstrating that convolution modules are effective in capturing local features of entities for entity classification.