## 5 Results and Discussion  
### Model Comparative Experiment  
The comparative experimental results of the model proposed and several existing baseline models are for the CMEIE dataset and DuIE2.0 dataset (Table 6.)  
Table 6 shows that the GPRL model proved effective on the CMEIE dataset, achieving a precision of 54.7%, a recall of 50.6%, and an F1 score of 51.7%. A comparative analysis shows that the entity relation joint extraction model outperforms traditional pipeline methods like NovelTagging, which demonstrates the issue of error propagation in pipeline approaches. The CasRel model transforms relations into a function mapping from head entities to tail entities, accurately identifying more entities in sentences. However, it falls short of recognizing nested entities, resulting in lower accuracy compared to the model in this work. TPLinker_plus can solve the nested entity problem but suffers from a more complex model structure leading to resource wastage, ultimately yielding lower recall and F1 scores. PRGC combines the strengths of CasRel and TPLinker. Inspired by PRGC's relation filtering, the GPRL model applies a similar approach. PRGC still inherits the complexity issues of TPLinker, leading to resource wastage, longer training times, and lower effectiveness. GPRL builds upon GPLinker by incorporating relation filtering and adopting a multi-task learning strategy to balance task losses. It achieves better precision, recall, and F1 score, with improvements of 26.1%, 8.3%, 2.2%, and 1.5% over other baseline models and a 2.1% improvement over the original GPLinker model in F1 score. To further validate the effectiveness of the model on Chinese datasets, comparative experiments were conducted on the DuIE2.0 dataset compared with other baseline models (Table 6). The GPRL model proves effective on the DuIE2.0 dataset, achieving a precision of 64.1%, a recall of 81.6%, and an F1 score of 71.8%. Compared to other baseline models, there are improvements of 16.7%, 10.5%, 2.8%, and 1.2% in F1 score, as well as a 1.4% improvement over the original GPLinker model. The original GPLinker model already excelled in model structure, training strategies, training speed, and results compared with other baseline models, thereby demonstrating the significance of the improvements made in this work to the GPLinker model. Furthermore, the experimental analysis indicates that model performance on the CMEIE dataset is generally lower due to the dataset's domain specificity in the medical field. The Chinese-Bert-WWM pre-trained language model used was trained on large-scale general corpora, leading to difficulties in semantic understanding of specialized medical data. The examples of unpredicted correct triples are shown in Table 7. In the triple (submucous cleft palate, related symptom, airway obstruction), \"submucous cleft palate\" is a rare medical term that the entity recognition failed to identify, resulting in the triple being unable to be predicted correctly. In the triple (gastritis (Chinese), Synonym, gastritis (English)), the Chinese expression for gastritis was not recognized as a synonym for its English expression. This is because Chinese-Bert-WWM is trained on a Chinese corpus, and its recognition of English terms needs to be improved. Hence, the Chinese-Bert-WWM model may exhibit a poor representation of medical data, resulting in errors or missed extractions of many triples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CMeIE**} & \multicolumn{3}{c}{**DuIE2.0**} \\ \cline{2-7}  & **P(\%)** & **R(\%)** & **F1(\%)** & **P(\%)** & **R(\%)** & **F1(\%)** \\ \hline NovelTagging & 51.4 & 17.1 & 25.6 & 60.6 & 45.3 & 55.1 \\ PRGC & 40.8 & 46.4 & 43.4 & 48.8 & 81.4 & 61.3 \\ CasRel & 50.5 & 48.5 & 49.5 & 62.0 & 80.4 & 70.6 \\ TPLinker\_plus & 50.7 & 49.8 & 50.2 & 64.3 & 74.4 & 69.0 \\ GPLinker & 51.2 & 47.5 & 49.6 & 61.7 & 81.0 & 70.4 \\ GPRL (our model) & 54.7 & 50.6 & 51.7 & 64.1 & 81.6 & 71.8 \\ \hline \hline \end{tabular}
\end{table}