## 4 Discussion
### Experiment Result
#### 4.2.2 Comparison with Other Models  
The proposed ER-LAC model was also compared with other SOTA models on three datasets. The results with the CoNLL04 dataset are shown in Table 4. Since other models reported the performance of the model using micro-averaging and macro-averaging metrics, we calculated the micro-averaged and macro-averaged evaluation metrics of the proposed model in the entity recognition and relation extraction subtasks. It can be seen that the proposed model can achieve the highest F1 score in the relation extraction subtask, with a macro F1 score of 75.13% and a micro F1 score of 73.04%, which are 2.26% and 1.57% better than the best-performing SOTA models, respectively. The micro F1 score of the proposed model in the entity recognition subtask reached the highest at 90.47%, which is 0.69% better than the best-performing model. Although the macro F1 score of entity recognition did not reach the highest level, it was second only to the TriMF model [22]. However, the same model has a much higher score in relation extraction than other models, indicating that the reason for the proposed model's macro F1 score not reaching the highest in entity recognition may be due to the fact that the entity-relation joint extraction task is more biased towards relation extraction.  
Table 5 shows the results of the proposed model and other SOTA models on the ADE dataset. The evaluation metrics for all models were based on macro-averaged values. It can be seen that the proposed model achieved the highest F1 scores in both entity recognition and relation extraction subtasks, with scores of 92.34% and 84.86%, respectively. The F1 score of the proposed model improved by 0.84% to 5.23% relative to the SOTA models in the entity recognition subtask, and by 0.96% to 7.57% in the relation extraction subtask. Moreover, the proposed model achieved the highest precision and recall in the entity recognition, as well as precision in the relation extraction.  
Figure 5: The confusion matrix of the models tested on dataset CoNLL04, where (**a**) is the entity classification result of the baseline model, (**b**) is the relation classification result of the baseline model, (**c**) is the entity classification result of our proposed model, and (**d**) is the relation classification result of our proposed model.  
Table 6 shows the results of the proposed model and other SOTA models on the SciERC dataset. All models' evaluation metrics use micro-averaged values. The proposed model achieves the highest F1 score in entity recognition, reaching 70.72%, which is an improvement of 0.19-5.52%, compared to other models. In the relation extraction subtask, the ER-LAC model is second only to the PL-Marker model, achieving the second highest F1 score. However, the proposed model performs better than the PL-Marker model in entity recognition. Therefore, this result may also be caused by the bias in the training of the two subtasks. Based on this result, it can also be seen that the proposed model has reached the level of the best SOTA model on the SciERC dataset. Overall, the proposed ER-LAC model outperforms, or is on par, with the current best SOTA models in all three public datasets.