## 3 Experiments
### Results  
We adopt _Precision_, _Recall_, and \(F1\) score to evaluate the performances [30]. A triplet is considered to be correctly extracted if and only if the head and tail entity and the relation type between them are exactly matched. Table 4 shows the experimental results on the two datasets. We can find that the experimental results of our model on the two datasets are better than other baseline models. The \(F1\) score of the SRAGNN model on the CEMR dataset is 1.88% better than that of the BERT-JEORE, and the \(F1\) score on the CMeIE dataset is 1.41% higher than the BERT-JEORE. The \(F1\) score of the SRAGNNs model is 3.91% better than the RSAN model on the CEMR dataset, and the \(F1\) score is 2.96% better than the RSAN model on the CMeIE dataset. The RSAN model also uses entity relations to guide entity recognition, but the design of the RSAN model is for the English dataset in the general domain, so when it is directly applied to the Chinese medical dataset, the experimental results of entity and relation extraction will not be optimal. WDec adopts the seq2 seq model to generate relation triplets and removes the duplicate triplets and fragmentary triplets via post-processing. Therefore, WDec achieves high precision on two datasets, while its recall is unsatisfactory, thus the \(F1\) score of the WDec model does not improve. To further explore the performance of SRAGNNs model for extracting Chinese medical entities and relations, we analyze the performance of different elements (H, R, T) in medical entity-relation triplets on the CEMR dataset, where H represents the head entity, T represents the tail entity, and R represents the entity relation. Table 5 shows the experimental results of the SRAGNNs model on triplets with different relations. From the experimental results in Table 5, it can be seen that the performance gap of the SRAGNNs model in recognizing only the head entity and only the tail entity is basically the same as the performance gap between (H, R) and (R, T). This demonstrates the effectiveness of the SRAGNNs model in recognizing head entity and tail entity. Furthermore, it can be found that the \(F1\) scores on (H, T) and (H, R, T) are quite different because the SRAGNNs model is based on relation-guided entity recognition. At the same time, the overlap of entity-relation triplets also directly leads to the correct extraction probability of entity pairs being greater than the correct extraction probability of entity-relation triplets. This also shows that in the process of entity-relation triplets extraction, most of the entity pairs are correctly recognized. To verify the ability of the SRAGNNs model to extract relational triplets from sentences with different degrees of overlap, we conducted further experiments on the CEMR dataset. The experimental results are shown in Table 6. The performance of several models, CopyRe, GraphRel, HRL, and ETL-Span, decreases as the number of relation triplets in the sentence increases. In contrast, the performance of the SRAGNNs model is significantly improved when extracting multiple triplets. The SRAGNNs model outperforms the BERT-JEORE model by 1.99% and 1.06%, respectively, when the overlapping relation triplets in a sentence are three and four. When a sentence contains three or more overlapping relation triplets, the SRAGNNs model outperforms other baseline systems. Therefore, the SRAGNNs model is more suitable for handling complex overlapping relation triplets than the baseline models.