## 2 Materials and Methods
### MGFGF-BiLSTM-EGP
The results indicate that each module significantly enhanced the model's performance, with the highest F1 score of 91.98% achieved when all three modules were integrated. #### 3.3.1 Effect of Pre-Trained Model

The pre-trained model substantially improved the overall performance. When only the pre-trained model (Model 1) was employed, the F1 score reached 88.01%, which was 5.69% higher than that of the model without the pre-trained model (82.32% for Model 2). This finding underscores the pre-trained model's effectiveness in capturing underlying features. The combination of the pre-trained model with the word encoder (Model 4) further increased the F1 score to 91.33%, representing a 9.01% improvement over Model 2 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & **Category** & **P \%** & **R \%** & **F1 \%** & **Macro P \%** & **Macro R \%** & **Macro F1 \%** \\ \hline \multirow{4}{*}{CDNER} & symptom & 86.99 & 88.26 & 87.6 & & & \\  & body part & 92.19 & 92.70 & 92.43 & & & \\  & disease & 92.29 & 91.70 & 92.98 & 91.42 & 92.18 & 91.98 \\  & drug & 92.50 & 93.97 & 93.22 & & & \\  & type & 93.11 & 94.25 & 93.68 & & & \\ \hline \multirow{4}{*}{CMeEE V2} & bod & 74.56 & 79.05 & 77.30 & & & \\  & dis & 78.73 & 87.85 & 83.81 & & & \\  & sym & 77.10 & 65.75 & 70.9 & & & \\  & ite & 70.87 & 55.58 & 62.04 & & & \\  & dru & 77.94 & 83.23 & 81.10 & 73.12 & 73.68 & 73.32 \\  & pro & 76.09 & 66.30 & 70.84 & & & \\  & mic & 69.56 & 86.60 & 78.19 & & & \\  & dep & 77.97 & 68.78 & 73.1 & & & \\  & equ & 55.23 & 70.01 & 62.62 & & & \\ \hline \multirow{4}{*}{CLUENER} & address & 76.81 & 80.71 & 78.71 & & & \\  & name & 92.67 & 84.76 & 88.54 & & & \\  & organization & 84.52 & 84.02 & 84.27 & & & \\  & game & 87.42 & 80.98 & 84.08 & & & \\  & scene & 81.75 & 76.87 & 79.23 & & & \\  & book & 88.04 & 73.64 & 80.20 & & 85.21 & 80.38 & 82.54 \\  & company & 84.09 & 88.10 & 86.05 & & & \\  & position & 86.92 & 81.88 & 84.33 & & & \\  & government & 82.72 & 85.90 & 84.28 & & & \\  & movie & 87.21 & 66.96 & 75.76 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Entity level evaluation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Pre-Trained** & **Word** & **Sentence** & **F1/\%** \\  & **Model** & **Encoder** & **Encoder** & **F1**/** \\ \hline
1 & ✓ & & & 88.01 \\
2 & & ✓ & & 82.32 \\
3 & & ✓ & ✓ & 82.67 \\
4 & ✓ & ✓ & 91.33 \\
5 & ✓ & & ✓ & 88.34 \\
6 & ✓ & ✓ & ✓ & 91.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study. (which used only the word encoder). This emphasizes the significance of pre-trained models in complex feature representation. When the pre-trained model was combined with both the word and sentence encoders (Model 6), the F1 score peaked at 91.98%, showing a 9.31% improvement over Model 3, which combined the word and sentence encoders. This result further demonstrates the pre-trained model's capacity to maximize the overall model performance. #### 3.3.2 Effect of Word Encoder

The word encoder plays a crucial role in enhancing the model's performance, especially when integrated with other modules. The F1 score for the word encoder alone (Model 2) was 82.32%, which, although lower than that of Model 1, which utilized only the pre-trained model, still illustrates the word encoder's value in word-level feature extraction. When the word encoder was combined with the pre-trained model (Model 4), the F1 score rose to 91.33%, a 3.32% improvement over Model 1. This finding indicates that the word encoder significantly contributes to refining the pre-trained model's fine-grained feature representation. In Model 6, where the word encoder was combined with both the pre-trained model and the sentence encoder, the F1 score further improved to 91.98%, a 3.64% increase over Model 5, which combined the pre-trained model and the sentence encoder. This demonstrates the pivotal role of the word encoder in a multi-module combination.