## 2 Materials and Methods
### MGFGF-BiLSTM-EGP
Although BERT-CRF lacked the SoftLexicon and BiLSTM modules present in the SLRBC model, it still performed competitively due to BERT's powerful representation capabilities, albeit with slightly lower metrics across all datasets. BERT-MRC, which reinterprets NER as a reading comprehension task, delivered adequate but not outstand ing results, with F1 scores of 82.93% on CDNER, 67.97% on CMeeE V2, and 76.89% on CLUENER. Its performance could potentially be improved by refining the description of entity types during MRC parameter settings. ### Entity Level Evaluation

Figure 7 and Table 4 present the entity-level evaluation results of the MFGFF-BiLSTM-EGP model across the CDNER, CMeeE, and CLUENER datasets, detailing the precision, recall, and F1 scores. In the CDNER dataset, the model demonstrated exceptional performance in recognizing the \"symptom\", \"body part\" \"disease\", \"drug\", and \"type\" categories, achieving F1 scores of 87.6%, 92.43%, 92.98%, 93.22%, and 93.68%, respectively. This consistency underscores the high quality of our dataset, indicating that the annotations are both balanced and precise, thereby facilitating robust model training. These results validate the model's superior performance in entity recognition within this dataset. For the CMeeE V2 dataset, the model's performance varied across different categories. It exceled in the \"dis\" (disease) and \"dru\" (drug) categories, with F1 scores of 83.81% and 81.1%, respectively. However, the model encountered difficulties in the \"equ\" (equipment) and \"ite\" (medical examination items) categories, where the F1 scores dropped to 62.62% and 62.04%, respectively. Notably, in the \"equ\" category, the model's precision was only 55.23%, likely due to the limited representation of specialized vocabulary within the general domain word vectors, resulting in weaker recognition performance in these areas. In the CLUENER dataset, the model showed a relatively balanced performance across categories, achieving high F1 scores in the \"company\", \"government\", and \"position\" categories of 86.05%, 84.28%, and 84.33%, respectively. This indicates strong recognition capabilities with minimal disparity between precision and recall, reflecting good stability. However, the model's performance in the \"movie\" and \"book\" categories was less satisfactory, achieving high F1 scores in the \"movie\" and \" \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CDNER**} & \multicolumn{3}{c}{**CMeeE V2**} & \multicolumn{3}{c}{**CLUENER**} \\ \cline{2-10}  & **P/\%** & **R/\%** & **F1/\%** & **P/\%** & **R/\%** & **F1/\%** & **P/\%** & **R/\%** & **F1/\%** \\ \hline Lattice LSTM & 82.43 & 84.51 & 83.46 & 61.26 & 62.33 & 61.79 & 71.69 & 70.78 & 71.23 \\ FLAT & 80.55 & 82.99 & 81.75 & 58.18 & 61.11 & 59.61 & 64.64 & 68.45 & 66.49 \\ BERT-CRF & 87.86 & 89.53 & 88.69 & 70.93 & 73.24 & 71.98 & 79.15 & 81.86 & 80.48 \\ BERT-MRC & 80.16 & 85.91 & 82.93 & 68.00 & 68.35 & 67.97 & 75.60 & 78.22 & 76.89 \\ SLRBC & 88.58 & 90.72 & 89.64 & 71.54 & 72.59 & 72.06 & 81.55 & 80.51 & 81.03 \\ MFGFF-BiLSTM-EGP (ours) & 91.42 & 92.18 & 91.98 & 73.12 & 73.68 & 73.32 & 85.21 & 80.38 & 82.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results of five commonly used obvious and our model on three datasets. Figure 7: Radar plots of the entity-level assessment results of the MFGFF-BiLSTM-EGP model on three datasets including precision, recall, and F1. factory, with F1 scores of 75.76% and 80.2%, respectively. Overall, the model maintained a balanced performance across multiple categories. ### Ablation Study

Table 5 shows the results of the ablation experiments performed by the MFGFF-BiLSTM-EGP model on the CDNER dataset to evaluate the impact of different module combinations on the model's F1 score. The study investigated the contributions of the pre-trained model, word encoder, and sentence encoder.