# Fd-CasBGRel: A Joint Entity-Relationship Extraction Model for Aquatic Disease Domains
## 5 Experiments and Analysis
### Ablation Experiment  
To evaluate the impact of the FD-Roberta-wwm-ext pretrained model, the BRC feature fusion module, and the GHM loss function on aquatic disease entity-relationship extraction, module ablation experiments were also carried out. The training outcomes are conveyed in Figure 11. Here, Casrel denotes the foundational framework, Casrel_BRC refers to the incorporation of only the feature fusion module, Casrel_GHM corresponds to the integration of solely the GHM loss function within the base framework, Casrel_FDRobertawwmext represents the sole addition of the fine-tuned pretraining model, and Fd-CasBGRel is the model proposed in this paper. We can see from Figure 11 that the Casrel model, when augmented with the fine-tuned pretraining model, demonstrates significant improvements over the baseline Casrel model in terms of precision (\(P\)), recall (\(R\)), and \(F1\) score metrics, as well as in convergence speed. This enhancement can be attributed to the fine-tuned model's ability to acquire domain-specific knowledge about aquatic diseases and more effectively encode the semantic features of those entities in comparison to the original pretraining model. Including the BRC feature fusion module alone leads to a notable increase in the model's \(p\)-value relative to the baseline model, suggesting that feature fusion enriches the semantic vector representations, thereby augmenting the accuracy of entity recognition. The R-value undergoes  
Figure 11: Ablation of the training process for experimental models based on Casrel. slight improvement, while the overall \(F1\) score surpasses that of the baseline model. Not surprisingly, incorporating the GHM loss function also contributes to performance gains to a certain extent by mitigating the data imbalance. Ablation experiments were also made on the test set, with these results presented in Table 9.  
Incorporating the feature fusion module, GHM loss function, and fine-tuned pretraining model individually into the model yields F1 scores of 75.86%, 73.67%, and 82.95%, respectively. This corresponds to an enhancement of 4.96%, 2.77%, and 12.05% vis-a-vis the baseline model, indicating that each of the three augmentations is beneficial to the model's performance. Notably, a greater improvement is gained from fine-tuning the pretraining model than from both the feature fusion module and GHM loss function. This can be ascribed to the pretraining model's better ability to learn disease text data features through fine-tuning, which provides stronger domain adaptation and more effective encoding of semantic vectors. The adoption of the feature fusion module, with its ensuing 4.96% improvement, demonstrates that fusing the relative position features of the head entity's first and last characters, local sentence features, and contextual features through CLN enables deeper feature integration and enriches semantic representations beyond the mere addition of head entity and sentence features. This confirms the efficacy of the BRC module. Moreover, integrating the GHM loss function results in a 2.77% improvement over the baseline model; this suggests the GHM loss function rectifies, to some extent, the issue of imbalanced aquatic disease data categories. When these three enhancements are applied in tandem, the model attains its peak \(F1\) score of 84.71% for the aquatic disease dataset, indicating a synergistic effect that collectively boosts the model's extraction capabilities. In this paper, the inputs to the proposed feature fusion module are processed through the self-attention mechanism, a BiLSTM network, and a multi-head mechanism that incorporates entity position computation.