# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 4 Results
### Ablation Experiment  
To further evaluate the effectiveness of our proposed model in improving entity-relation extraction performance, ablation experiments were conducted on the DUIE and NYT datasets. The specific results are shown in Tables 4 and 5, where \"ours-1\" denotes the removal of the dependency syntax analysis module, and \"ours-m\" denotes the removal of the mixture-of-experts architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **F1/\%** \\ \hline OneRel & 70.81 & 74.55 & 72.63 \\ Ours-1 & 75.15 & 76.26 & 75.70 \\ Ours-m & 75.65 & 75.53 & 75.59 \\ Ours & **76.87** & **76.40** & **76.63** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment results of the model on DUIE.  
Figure 6: The loss curve of NYT dataset on the proposed methodology.  
By comparing the ablation experiment results, it is observed that removing either the dependency syntax analysis module or the mixture-of-experts architecture leads to a decline in model performance. Specifically, after removing the syntactic information module, the F1 scores of the model decreased by 2.30% and 0.83%, respectively, on the DUIE and NYT datasets. After removing the mixture-of-experts architecture, the F1 scores, respectively, decreased by 1.04% and 0.19% on the DUIE and NYT datasets. The model proposed in this paper, by constructing a dependency-based graph neural network, clearly captures the dependency relationships between words in a sentence, particularly the dependency paths between entities, providing important prior information and semantic cues for the relation extraction task. Notably, compared to English texts, Chinese sentence structures are more complex, and word dependencies are often not confined to adjacent characters but may span across multiple characters or even phrases. The incorporation of the additive attention mechanism for feature fusion allows for a better capture of long-range dependencies between characters in a sentence. An imbalance in the distribution of relationship types in the dataset is a common issue. Introducing the mixture-of-experts (MoE) architecture can effectively mitigate conflicts between categories. Sentences containing relationship types with a higher number of occurrences are often more common in real-world texts and tend to include multiple triples, increasing the difficulty of correctly identifying all triples. On the other hand, relationship types with fewer occurrences suffer from limited training data, which leads to insufficient understanding of these relationships by the model and poorer performance in extracting the corresponding triples. The mixture-of-experts architecture dynamically generates allocation weights for the input data across different experts through a gating network, enabling each expert to effectively model the features of different relationship types. This results in a more accurate capture of specific semantic information and entity relationships. Especially in scenarios with high label diversity or an uneven relation distribution, this architecture can dynamically adjust the learning weights for different relations, effectively alleviating conflicts among categories. The experimental results confirm that both the syntactic information module and the hybrid expert architecture significantly improve the model's accuracy.