## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.4 Experimental Results on the Public Dataset  
To validate the generalization performance of the MF-SERel model on public datasets, we selected the Chinese information extraction dataset DuIE [42; 43], released by Baidu, and the Chinese medical dataset CMeIE [44] for experiments. The model was compared with corresponding state-of-the-art (SOTA) models, including GraphRel1p, GraphRel2p, CasRel, TPLinker, and BiRTE, as shown in Table 6.  
As shown in Table 6, the MF-SERel model achieved the best performance in terms of precision and F1 score on the DuIE dataset, registering improvements of 3.4 percentage points and 1.7 percentage points, respectively, compared to the TPLinker model. In terms of recall, the model's performance was 0.4 percentage points lower than that of TPLinker. This issue may be due to label assignment errors, as the scoring function incorrectly predicted labels, resulting in some entities not being accurately extracted and decreasing recall. Despite this, the MF-SERel model enhanced precision while maintaining a recall rate close to that of TPLinker and achieved a higher F1 score, reflecting its superior overall predictive performance. Meanwhile, the experimental results for the Chinese medical dataset CMeIE showed that the MF-SERel model achieved superior performance across all metrics. Specifically, the F1 score improved by 23.1, 8.8, and 2.5 percentage points compared to the multi-stage GraphRel2p, CasRel, and BiRTE models, respectively. The medical domain data were inherently complex, containing many specialized terms, making it challenging to represent using single semantic features. Unlike the baseline models, the proposed model incorporated syntactic structural features, providing richer representational capacity and improving recognition performance in complex scenarios. The experiments on this dataset further demonstrated that the single-stage MF-SERel model effectively avoided  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**DuIE**} & \multicolumn{3}{c}{**CMeIE**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline GraphRel1p & 0.402 & 0.490 & 0.442 & 0.375 & 0.427 & 0.399 \\ GraphRel2p & 0.499 & 0.526 & 0.512 & 0.426 & 0.443 & 0.434 \\ CasRel & 0.631 & 0.625 & 0.628 & 0.584 & 0.571 & 0.577 \\ TPLinker & 0.622 & 0.730 & 0.672 & 0.592 & 0.642 & 0.616 \\ BiTRE & 0.641 & 0.712 & 0.675 & 0.621 & 0.658 & 0.640 \\ MD-SERel & 0.656 & 0.726 & 0.689 & 0.648 & 0.683 & 0.665 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Generalization experiment results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline -BERT & 0.948 & 0.919 & 0.933 \\ -GCN & 0.980 & 0.930 & 0.954 \\ -ATT & 0.971 & 0.940 & 0.955 \\ MD-SERel & 0.984 & 0.948 & 0.966 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments’ results.  
exposure bias. In summary, the MF-SERel model not only performed exceptionally well on the general domain dataset DuIE but also maintained high performance on the complex medical domain dataset CMeIE, showing robust generalization capabilities.