## 5 Experimental Section
### Analysis and Discussion  
#### 5.3.1 Ablation Study  
In this section, ablation experiments were conducted to demonstrate the necessity of the entity and relation heterogeneous graph attention network in ERHGA. Table 6 shows the results of different numbers of entity and relation heterogeneous graph attention network layers. As the number of entity and relation heterogeneous graph attention network layers increases, the F1 score first increases and then decreases. For the WebNLG dataset, when the number of layers is 2, the F1 is the best. This illustrates that setting the number of layers to 2 is sufficient and most suitable for the WebNLG dataset. The joint entity and relation extraction method based on entity and relation heterogeneous graph attention networks, where each node learns the semantic information associated with it and updates its own representation, resulted in better performance of the model. However, with the increasing number of layers, the nodes exchanged too much information with one another, absorbing some irrelevant information and impairing the model's performance. Table 7 displays the results of the other hyperparameter settings. Here, 16 groups of hyperparameter settings are taken as examples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **WebNLG** \\ \hline
0 & 91.8 \\
1 & 92.5 \\
2 & **93.3** \\
3 & 91.9 \\ \hline \hline \end{tabular} Bold marks the highest score.  
\end{table}
Table 6: F1 (%) of different numbers of entity and relation heterogeneous graph attention network layers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Different Hyperparameters.**} & \multicolumn{1}{c}{**WebNLG**} \\ \hline Training batch size = 6, Learning rate = 0.1, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.8 & 91.8 \\ Training batch size = 6, Learning rate = 0.1, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.9 & 87.2 \\
**Training batch size = 6, Learning rate = 0.1, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.8** & **93.3** \\ Training batch size = 6, Learning rate = 0.1, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.9 & 91.6 \\ Training batch size = 6, Learning rate = 0.01, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.8 & 91.7 \\ Training batch size = 6, Learning rate = 0.01, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.9 & 92.3 \\ Training batch size = 6, Learning rate = 0.01, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.8 & 91.5 \\ Training batch size = 6, Learning rate = 0.01, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.9 & 90.9 \\ Training batch size = 8, Learning rate = 0.1, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.8 & 92.9 \\ Training batch size = 8, Learning rate = 0.1, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.9 & 92.4 \\ Training batch size = 8, Learning rate = 0.1, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.8 & 92.7 \\ Training batch size = 8, Learning rate = 0.1, Weight decay = 2 \(\times\)\(10^{-4}\), Momentum = 0.9 & 92.0 \\ Training batch size = 8, Learning rate = 0.01, Weight decay = 1 \(\times\)\(10^{-4}\), Momentum = 0.8 & 91.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: F1 (%) of different hyperparameter settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-4}  & **Normal** & **SEO** & **EPO** \\ \hline CopyRE & 59.2 & 33.0 & 36.6 \\ GraphRel & 65.8 & 38.2 & 40.6 \\ CasRel & 89.4 & 92.2 & 94.7 \\ TPLinker & 87.9 & 92.5 & 95.3 \\ RIFRE & 90.1 & 93.1 & 94.7 \\ PRGC & 90.4 & 93.6 & 95.9 \\ SGNet & 89.2 & 92.5 & 95.0 \\ \hline ERHGA & **91.2** & **93.7** & **95.9** \\ \hline \hline \end{tabular} Bold marks the highest score, while underline marks the second best score.  
\end{table}
Table 5: F1 (%) on sentences with different overlapping types and different triple numbers.