# Fd-CasBGRel: A Joint Entity-Relationship Extraction Model for Aquatic Disease Domains
## 5 Experiments and Analysis
### Experiment on Relationship Overlap  
Besides improving the relationship extraction, another critical issue that our model addresses is the problem of relationship overlap. To assess the model's extraction capabilities under conditions of overlapping relationships, we categorized the test set data into two classes, Normal and Single Entity Overlap (SEO), based on the presence of overlapping relationships. The Normal category has 830 data points, while the SEO category has 196 data points (see Table 3). Figure 10 shows the experimental outcomes. Evidently, the \(F1\) scores of the model proposed in this study are superior to those of the other nine models for both the Normal and SEO categories tested. The performance of those latter models for these two relationship extraction types displays a general reduction, which suggests the task of extracting entity-relationship triplets gets harder with a greater proportion of shared entities in the dataset. Specifically, NoverTagging, CopyR, and GraphRel models undergo a marked decline in \(F1\) scores when dealing with data that \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AquaticDiseaseRE**} & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline NoverTagging & 48.66 & 38.95 & 43.27 & 52.58 & 19.36 & 28.30 \\ CopyR-Muti & 50.07 & 33.30 & 40.00 & 37.76 & 36.48 & 37.11 \\ CopyR-One & 48.95 & 31.51 & 38.34 & 32.24 & 28.91 & 30.48 \\ GraphyRel-1p & 61.01 & 43.13 & 50.54 & 42.36 & 39.24 & 40.74 \\ GraphyRel-2p & 62.36 & 44.23 & 51.75 & 44.73 & 41.15 & 42.87 \\ TPLinker & 74.29 & 71.64 & 72.94 & 89.14 & 84.79 & 86.91 \\ Casrel & 73.17 & 68.77 & 70.90 & 94.17 & 91.36 & 92.74 \\ PFN & 77.31 & 74.84 & 76.05 & 94.72 & 92.37 & 93.53 \\ UniRel & 76.32 & 73.68 & 74.98 & 94.88 & 94.63 & 94.75 \\ Fd-CasGRel & **83.15** & **86.33** & **84.71** & - & - & - \\ Wnt-CasBGRel * & - & - & - & **96.79** & **95.46** & **96.12** \\ \hline \hline \end{tabular}  
*: \"W-CasBGRel denotes the English version of the model in this paper, where the pre-trained model of the coding layer was replaced with the English version and fine-tuned within the English dataset used, and the rest is consistent with the Fd-CasBGRel model.  
\end{table} 
Table 7: Evaluation results for the 10 experimental models. includes relationship overlap, indicating their limitations in handling such overlapping forms of data. By contrast, the remaining models actually exhibit slightly improved performance for overlapping relationship data, which we attribute to their respective architectures' capability to address that key issue. The Fd-CasBGRel model developed in this study has the best results for both Normal- and SEO-type relationship extraction tasks. It even records higher \(F1\) scores in SEO- than Normal-type data, which could be due to the smaller sample size of SEO-type in the test set. Overall, these experimental outcomes provide compelling evidence that the model presented in this paper is more adept at resolving the challenge of relationship overlap in the task of aquatic disease relationship extraction. To further validate the model's extraction performance across scenarios with varying numbers of triplets, we categorized the data into five classes based on their quantity. These results are presented in Table 8.  
A discernible trend emerges where the \(F1\) scores of both NoverTagging and CopyR models diminish to varying extents as the dataset's triplets increase in frequency. This trend suggests a greater difficulty in processing semantic information and navigating a more complex answer space when more triplets are present, leading to potential confusion and errors during extraction. Additionally, the occurrence of overlapping relation triplets further complicates the extraction process. Conversely, models such as TpLinker, Casrel, PFN, and Unirel have significantly improved \(F1\) scores when the triplet count is between 2 and 4.