## 4 Experiments
### Comparative Models  
To evaluate the performance of the model proposed, comparisons were made with the following state-of-the-art entity relation extraction models. Choosing these models for comparison allows for a comprehensive and in-depth evaluation of the performance of the model. It also highlights the innovative points and advantages of our model through the comparison with different technical approaches and methods:  
* NovelTagging: This model transforms the entity relation extraction problem into a sequence labeling problem, enabling the simultaneous recognition of entities and relations. While it does not address the issue of overlapping triples, it provides a foundational reference for our research.
* CasRel: Based on sequence labeling, this model introduces a cascaded binary tagging framework to tackle the problem of overlapping triples, which has become a critical milestone in the field.
* TPLinker_plus: This model proposed a novel tagging scheme, which not only resolves overlapping triple issues, but also addresses entity nesting problems.
* PRGC: Based on a table-filling approach, this model introduces a new end-to-end framework that can resolve overlapping triple problems by separately predicting entities and relations. The model reduces redundant information generation and offers us a different perspective.
* GPLinker: Utilizing a global pointer-based model with matrix labeling, the model enables entity relation prediction from a global perspective. It successfully tackles overlapping triple issues and also resolves entity nesting problems, and it is taken as an important reference model for us.  
In the context of the aforementioned model experiments, the specific parameters are detailed in Table 5. Given the substantial memory consumption of the TPLinker-plus and PRGC models, when the batch size was set to 16, the models encountered a memory overflow error, exceeding the available memory capacity of the current runtime environment. So, the batch size was adjusted to 8 for both TPLinker-plus and PRGC models. All other parameters were kept consistent with those described in our model, thereby facilitating a more equitable execution of the comparative experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Meaning** \\ \hline maxlen & 300 & Truncation length of the text \\ batch\_size & 16 & Batch size of training data \\ epochs & 100 & Number of rounds of training iterations \\ learning rate & \(2\times 10^{-5}\) & Initial learning rate \\ pool\_kernel\_size & 2 & Pooling layer size \\ gp\_threshold & 0 & Entity–relationship extraction threshold \\ relation\_threshold & 0.9 & Relationship filtering threshold \\ relation\_adding & 0.3 & Relationship filtering threshold \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of our model parameter values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **NovelTagging** & **CasRel** & **TPLinker-Plus** & **PRGC** & **GPLinker** \\ \hline maxlen & 300 & 300 & 300 & 300 \\ batch\_size & 16 & 16 & 8 & 8 & 16 \\ epochs & 100 & 100 & 100 & 100 & 100 \\ learning rate & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of comparison models’ parameter values.