## 4 Experiments and Analysis of Results
### Analysis of Results  
#### 4.4.1 Performance Comparison of Different Models  
To validate the entity relation extraction performance of the MD-SERel model on the pesticide dataset PRD, several state-of-the-art (SOTA) models from recent years--GraphRel1p, GraphRel2p, CasRel, TPLinker, and BiRTE--were selected as comparison models, and experiments were conducted accordingly. The following section provides a brief introduction of the comparison models:  
(1) GraphRel [23]: A two-stage joint entity and relation extraction model based on a GCN. GraphRel1p uses a bidirectional RNN and a bidirectional GCN to initially predict the relations between token pairs and the entity labels of tokens. GraphRel2p builds on GraphRel1p using a relation-weighted GCN to further account for the interaction between entities and relations in prediction results. (2) CasRel [16]: A multi-module, multi-stage joint extraction model that uses a cascaded binary tagging scheme, labeling only the beginning and the end of entities. It is particularly effective at handling overlapping triples. (3) TPLinker [22]: A single-stage joint extraction model that transforms the joint extraction task into a label pair connection problem handshaking labeling. This approach resolves overlapping triplets and avoids exposure bias. (4) BiRTE [19]: A multi-stage joint extraction model that employs a bidirectional complementary entity pair matching strategy, reducing dependence on head entity extraction results and effectively improving triplet recognition. The above models, along with the MD-SERel model, were tested on the pesticide dataset PRD. The results for each evaluation metric are presented in Table 3.  
Based on the model comparison results shown in Table 3, we observed that the MD-SERel model outperformed GraphRel, CasRel, and TPLinker across all metrics. Compared to the GraphRel2p model, the MD-SERel model achieved a 23.3-percentage-point improvement in F1 score. This is because the multi-stage GraphRel model first extracted relations and then extracted head and tail entities based on these relations, leading to exposure bias. Compared to those of the CasRel and BiRTE models, the F1 score of MF-SERel improved by 3.4 and 1.5 percentage points, respectively. The BiRTE model extracted triplets from two complementary directions, partially alleviating exposure bias caused by CasRel's sole reliance on head entity extraction, improving performance. However, its multi-stage extraction strategy still exhibited exposure bias. Additionally, both CasRel and BiRTE used \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline GraphRel1p & 0.640 & 0.753 & 0.692 \\ GraphRel2p & 0.688 & 0.783 & 0.733 \\ CasRel & 0.977 & 0.891 & 0.932 \\ TPLinker & 0.949 & 0.936 & 0.943 \\ BiRTE & 0.962 & 0.940 & 0.951 \\ MD-SERel & 0.984 & 0.948 & 0.966 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison using pesticide datasets. BERT for single-semantic-feature extraction, whereas our model, MF-SERel, employed RoBERTa and integrated semantic and syntactic features, enriching the corpus representation and enhancing triplet extraction performance. Compared to TPLinker, the MD-SERel model achieved a 2.3-percentage-point improvement in F1 score because TPLinker has weaker generalization capabilities for varying entity lengths, preventing it from addressing heterogeneity in entity lengths present in pesticide registration data. Additionally, the text features extracted by TPLinker were limited, preventing it from effectively capturing long-distance dependencies, leading to suboptimal model performance. The proposed HT-BES multi-dimensional labeling strategy effectively identified various triplets by detecting entity boundary labels and positional labels. At the same time, the introduction of attention mechanisms and syntactic structure information into the encoding layer enhanced feature representation capabilities and long-distance entity capture, improving model accuracy.