# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 4 Results
### Comparison with Basic Models  
To evaluate the superiority of our proposed entity-relation joint extraction model, we conducted comparative experiments with CasRel [14], TPLinker [33], CasDE [34], and OneRel [15] on the DUIE and NYT datasets.  
1. CasRel: a model that utilizes BERT as the encoder and employs a multi-module, multi-step approach for relation triplet extraction.
2. TPLinker: a model that introduces a handshake annotation method that enables the extraction of triplets in a single decoding pass, while addressing issues such as overlapping relations and exposure bias.
3. CasDE: this method leverages a text-specific relation decoder to detect relations within sentences and uses a span-based labeling scheme to identify the corresponding head and tail entities.
4. OneRel: a single-step, single-module joint extraction strategy that reformulates the joint extraction task into a fine-grained triplet classification problem.  
The experimental results are shown in Tables 2 and 3, which indicate that our model consistently outperforms various baseline models on both datasets in terms of accuracy  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Training Set** & **Validation Set** & **Test Set** & **Relation Type** \\ \hline DUIE & 169,744 & 20,497 & 20,500 & 48 \\ NYT & 56,195 & 5000 & 5000 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
and F1 score. Specifically, on the DUIE dataset, our model achieves remarkable results, with an accuracy of 76.87%, a recall of 76.40%, and an F1 score of 76.63%, improving by 6.06%, 1.85%, and 4.00%, respectively, compared to the backbone model OneRel. On the NYT dataset, the model achieves an accuracy of 93.6%, a recall of 92.1%, and an F1 score of 92.8%, with improvements of 1.4%, 1.5%, and 1.4%, respectively, over the backbone model OneRel. We calculated the confidence intervals for the precision, recall, and F1 score of the proposed model using bootstrapping. The results show that our model has relatively narrow confidence intervals, indicating stable performance. The validation loss curves for the proposed approach are shown in Figures 5 and  
To further verify whether the performance differences between the models are statistically significant, we used the paired t-test to analyze the experimental results. When the \(p\)-value is smaller than the significance level (typically 0.05), the null hypothesis can be rejected, indicating a significant difference between the samples. On the DUIE dataset, based on the F1 scores between our model and OneRel, the paired t-test results show that the performance difference is significant (T-statistic = 5.56; \(p\)-value = 0.005). Similarly, on the NYT dataset, we conducted a similar test, and the results show that the F1 score difference between our model and OneRel is also significant (T-statistic = 4.24; \(p\)-value = 0.013). These statistical analyses further confirm the superiority of our model on both datasets.  
The performance improvement of our model is more prominent on the DUIE dataset. This is due to not only the large scale of the DUIE dataset but also the higher linguistic complexity of the extraction tasks compared to English datasets as a Chinese dataset. The model proposed in this study significantly improves the ability to model complex relationships in Chinese data by incorporating dependency-graph-structure information and deeply integrating it with contextual semantic features. Additionally, the DUIE dataset contains a greater variety of relationship types, and the mixture-of-experts architecture is able to select the most suitable expert model for recognizing complex relationship types, thereby capturing and identifying specific entity relationships more accurately.