## 5 Results and Discussion
### Ablation Experiment  
To analyze the contributions of each module in the entity relation joint extraction model constructed in this research, relevant ablation experiments were conducted on CMeIE and DuIE2.0 datasets. The performance indicators of different modules are shown in Table 9. The results indicate that both the relationship filtering and the loss function optimization integrated into this model have been proven to be effective.  
Relationship filtering can reduce relationship redundancy and improve the model's prediction accuracy. Table 9 shows that the relationship filtering component increased precision by 2.3%, recall by 0.8%, and F1 score by 1.2% on the CMeIE dataset. On DuIE2.0 dataset, precision increased by 1.9%, recall by 0.3%, and F1 score by 1.1%. The main reason is that predicting triplets requires judgment on many relationships, and the original model consumes more resources and time, leading to a decrease in accuracy. Optimizing the loss function dynamically adjusts the weights of multiple tasks based on noise during the model training process, ultimately enhancing model performance. As shown in Table 9, adding the loss function optimization component increased precision by 1.2%, recall by 2.3%, and F1 score by 0.9% on the CMeIE dataset. On the DuIE2.0 dataset, precision increased by 0.5%, recall by 0.3%, and F1 score by 0.3%. The results show that adding components to GPRL on different datasets led to improvements, with the relationship filtering module having a greater impact on GPRL and the effect of loss function optimization being relatively smaller but still contributing to enhancements, indicating that both modules are effective for GPRL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Examples of Unpredicted Correct Triples** & **Example** \\ \hline (Diseases, Related (Symptoms), Diseases) & (submucous cleft palate, related (symptom), airway obstruction) \\ (Symptoms, Synonyms, Symptoms) & (gastritis (Chinese), Synonym, gastritis (English)) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Examples of unpredicted correct triples.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CMeIE**} & \multicolumn{3}{c}{**DuIE2.0**} \\ \cline{2-7}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline HNNERJE [42] & 48.49 & 46.37 & 47.40 & / & / & / \\ FA-CBT [43] & 54.51 & 48.63 & 51.40 & / & / & / \\ DEPR [44] & 47.51 & 46.11 & 46.80 & 71.06 & 65.35 & 68.09 \\ REACT [45] & / & / & / & 68.5 & 66.0 & 67.2 \\ CasRelBLCF [46] & / & / & / & 74.0 & 68.6 & 71.2 \\ GPRL (our model) & 54.7 & 50.6 & 51.7 & 64.1 & 81.6 & 71.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of existing research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CMeIE**} & \multicolumn{3}{c}{**DuIE2.0**} \\ \cline{2-7}
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline GPlinker & 51.2 & 47.5 & 49.6 & 61.7 & 81.0 & 70.4 \\ GPlinker + Relation filter & 53.5 & 48.3 & 50.8 & 63.6 & 81.3 & 71.5 \\ GPlinker + Relation filter & 54.7 & 50.6 & 51.7 & 64.1 & 81.6 & 71.8 \\ + Loss optimization & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of ablation experiments.