## 4 Experiments
### Results  
The performance of our model on the nested entity recognition task is shown in Table 2. The results demonstrate that our model effectively improves the performance of the nested entity recognition task. Our model achieves F1 values of 86.66%, 85.83%, and 81.33% on the three datasets, which are 0.06%, 0.43%, and 1.32% higher than the state-of-the-art baseline model, respectively. In particular, [PERSON] et al. [31] used a multi-task learning model with a biaffine attention mechanism and boundary recognition for nested entity recognition. However, our model performed significantly better on all three datasets, demonstrating that relative position and local features within the entity can be more effective for nested entity recognition. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **Epochs** & \begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Bert Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **LSTM** \\ **Layer** \\ \end{tabular} & \begin{tabular}{c} **LSTM** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Start/Tail** \\ **FFNN Size** \\ \end{tabular} & \
\begin{tabular}{c} **Warm** \\ **Factor** \\ \end{tabular} &
\begin{tabular}{c} **Gradient** \\ **Clipping** \\ \end{tabular} \\ \hline ACE2004 & 15 & 8 & \(5\times 10^{-4}\) & \(5\times 10^{-6}\) & \(1\) & 768 & 384 & 0.1 & 1.0 \\ ACE2005 & 15 & 12 & \(5\times 10^{-4}\) & \(5\times 10^{-6}\) & \(1\) & 768 & 384 & 0.1 & 1.0 \\ GENIA & 10 & 8 & \(5\times 10^{-4}\) & \(5\times 10^{-6}\) & \(1\) & 512 & 256 & 0.1 & 5.0 \\ MSRA & 10 & 6 & \(1\times 10^{-3}\) & \(5\times 10^{-6}\) & \(1\) & 512 & 256 & 0.1 & 5.0 \\ Resume & 10 & 12 & \(5\times 10^{-3}\) & \(5\times 10^{-6}\) & \(1\) & 512 & 256 & 0.1 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter settings. Table 3 shows the results of our model and the baseline model on the flat entity recognition task. Our model achieves the best performance on the MSRA and Resume datasets with F1 values of 95.79% and 96.47%, precision of 95.83% and 96.62%, and recall of 95.76% and 96.32%, respectively. The comparison models used are all based on the sequence labeling method, but our model not only improves in precision but also increases in recall. This proves that our method is applicable not only to nested entities but also to flat entities. To demonstrate the importance of relative position features and local features in the entity recognition task, ablation experiments were carried out according to entity type. The results of the ablation experiments on the ACE2004 dataset are shown in Table 4, where BAM indicates that only the biaffine attention mechanism is used; RoPE-BAM indicates that the capture of relative position features is achieved by rotary position embedding; and CBAM-RoPE-BAM indicates that the local features are enhanced using the convolution module on top of RoPE-BAM. The results show that the F1 values of CBAM-RoPE-BAM and RoPE-BAM are 1.67% and 0.49% higher than those of BAM, respectively, and the performance is improved for all seven types of entity recognition. We believe this is because the relative position feature can effectively improve the precision of the model. At the same time, the local features within the entity can help the model to perform entity recognition more comprehensively. As a result, the recall of the model has also improved.