## 4 Experiments
### Main Results  
In this section, we compare and analyze the performance of SLRBC with other baseline models. The experimental results are shown in Table 5. It is noticeable that when compared to these baseline models, SLRBC has enhanced the F1 score on the CCKS2018 dataset by 7.81%, 9.71%, 0.8%, 1.62%, and 0.78%, respectively, and on the CCKS2019 dataset by 16.93%, 2%, 1.43%, 2.36%, and 1.32%, respectively.  
Through horizontal comparison, we observe that the performance of all models is superior on CCKS2018 compared to CCKS2019. This discrepancy is evidently due to variations in the granularity of entity annotation between the two datasets. The clearer the annotation of training data, the better the performance of the model. Among these models, the CNN-CRF model displays the most prominent performance variation between the two datasets. Its performance on CCKS2019 notably lags behind that of the other models. This discrepancy arises from the fact that the named entities in CCKS2019 are longer, and the CNN model struggles to capture long-distance dependencies effectively. It is evident that our SLRBC can effectively address this issue.  
Through vertical comparison, we observe a significant improvement in model performance after incorporating SoftLexicon compared to the traditional CNN and BiLSTM model structures. This result further validates the positive impact of the purpose-driven approach on enhancing NER accuracy. Simultaneously, it is evident that the F1 scores for BERT-BiLSTM-CRF and SoftLexicon-BERT with PLM, as well as our SLRBC, are notably superior to those without PLM. It proves the effectiveness of introducing large PLMs. The remarkable performance of SLRBC underscores the effectiveness of the model structure we proposed, which integrates SoftLexicon and RoBERTa in the realm of Chinese electronic medical record NER.  
In addition, we conduct a comparison of the training speed and testing speed of each model, as shown in Table 6. It can be found that the model without BERT or RoBERTa, including CNN-CRF, BiLSTM-CRF, and SoftLexicon-BiLSTM-CRF, has faster processing speed, primarily due to the simpler model structures and fewer parameters. However, SLRBC operates at a slower pace, indicating that RoBERTa, with its increased parameter count, contributes to the slower processing speed compared to BERT. Meanwhile, the output vector dimension of BERT is 768, while the output vector dimension of RoBERTa is 1024, which further increases the overall computational load of the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CCKS2018**} & \multicolumn{3}{c}{**CCKS2019**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline CNN-CRF & 83.72\% & 90.88\% & 87.16\% & 61.97\% & 76.49\% & 68.47\% \\ BiLSTM-CRF & 86.39\% & 84.15\% & 85.26\% & 82.92\% & 83.88\% & 83.40\% \\ BERT-BiLSTM-CRF & 94.37\% & 93.87\% & 94.17\% & 83.81\% & 84.14\% & 83.97\% \\ SoftLexicon-BiLSTM-CRF & 93.22\% & 93.48\% & 93.35\% & 81.98\% & 84.14\% & 83.04\% \\ SoftLexicon-BERT & 94.06\% & 94.32\% & 94.19\% & 83.21\% & 84.97\% & 84.08\% \\
**SLRBC** & **94.70\%** & **95.23\%** & **94.97\%** & **84.62\%** & **86.19\%** & **85.40\%** \\ \hline \hline \end{tabular}  
* The winner is in bold.  
\end{table}
Table 5: Experimental results of different models on CCKS2018 and CCKS2019.