## 4 Experiments and Analysis
### Comparison of Different Methods  
In order to validate the effectiveness of the PTKRE model on the relational extraction dataset, we conducted this part of the experiment on the SemEval 2010 Task 8 dataset. We compared it with other relational extraction models.  
1. The R-BERT [11] model adds different special labels for the head and tail entities, enriching the pre-trained BERT model by using the entity information for the relationship classification task.
2. The A-GCN [8] model utilizes dependency information for relationship classification. The attention mechanism is applied to dependency connections by assigning weights for both connections and types to distinguish the importance of dependency information better.
3. The Skeleton-Aware BERT [20] model proposes an indicator-aware relation extraction method in order to be able to utilize both syntactic indicators and sentence context. First, this model extracts the syntactic indicators under the guidance of syntactic knowledge. Then, a neural network is constructed to combine the syntactic indicators and the whole sentence to represent the relation better.
4. The KLG [16] model utilizes Top-K prediction sets to improve performance on the relational extraction task. First, a pre-trained language model is fine-tuned on the downstream dataset, and this PLM automatically generates Top-K prediction sets for each sample, where a dynamic K selection mechanism generates K. Then, a labeled graph neural network is constructed.
5. The PTR [13] method is a prompt-based learning approach that proposes to encode the a priori knowledge of a classification task into rules, then design sub-prompts based on the rules and apply a masked training task of a language model to predict the classification.
6. The RIFRE [21] model proposes an iterative fusion method for representations based on heterogeneous graph neural networks. The method takes relations and words as nodes on the graph. It iteratively fuses the two types of semantic nodes through a message-passing mechanism to get a representation of the more suitable node for the relation extraction task. When the node representation is updated, the model does the relation extraction task.  
As seen from the experimental results in Table 9, the F1 score of the PTKRE model is improved compared to the comparison models. R-BERT is a more classical relational extraction model after the emergence of the pre-trained language model BERT, and the PTKRE model improves the F1 score by 2.13 compared to the R-BERT model, which illustrates the effectiveness of the PTKRE model in the relational extraction task.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{Sentence:} & His trademark steam-engine puffing is \\  & revealed as a \textless{}el\textgreater{} sound \textless{}/el\textgreater{} made by a \\  & \textless{}e2\textgreater{} viper \textless{}/e2\textgreater{} splitting venom at his prey \\  & before swallowing her whole. \\ Truth label: & Cause–Effect (e2, e1) \\ PTKRE-Pos Predictive labels: & Cause–Effect (e1, e2) \\ PTKRE Predictive labels: & Cause–Effect (e2, e1) \\ \hline \hline \end{tabular}  
* A “green bean” which is actually a \textless{}el\textgreater{} fruit \textless{}/e1\textgreater{} with \textless{}e2\textgreater{} seeds \textless{}/e2\textgreater{} inside.
* Truth label: & Component–Whole (e1, e2) \\ PTKRE-Pos Predictive labels: & Component–Whole (e2, e1) \\ PTKRE Predictive labels: & Component–Whole (e1, e2) \\ \hline \hline \end{tabular}  
\end{table}
Table 8: Comparison of correct results and model predictions.