## 4 Discussion
### Experiment Result
#### 4.2.3 Complexity Analysis  
Although the superior performance of the proposed ER-LAC model has been validated in both ablation experiments and comparison experiments with other SOTA models, the model introduces more feature extraction and selection modules compared to the baseline model SpERT, which may significantly increase the training and inference time and make it difficult to be deployed in practice. Therefore, to demonstrate that the ER-LAC model still has lower resource consumption and higher computational speed, we conducted a complexity analysis on the model, primarily comparing the parameter size, average sample training time, and inference time between ER-LAC and the baseline model. Table 7 shows the results of the complexity analysis experiments. The average sample training time is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**CoNLL04**} \\ \cline{2-7}  & \multicolumn{3}{c}{**NER (\%)**} & \multicolumn{3}{c}{**RE (\%)**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline Global [28] & - & - & 85.60 & - & - & 67.80 \\ Multi-turn & 89.00 & 86.60 & 87.80 & 69.20 & 68.20 & 68.90 \\ QA [29] & 85.78 & 86.84 & 86.25 & 74.75 & 71.52 & 72.87 \\ SpERT [14]* & 88.25 & 89.64 & 88.94 & 73.04 & 70.00 & 71.47 \\ Deeper [30] & 89.72 & 86.42 & 87.00 & 77.73 & 68.38 & 72.63 \\ Deeper [30]* & 89.84 & 89.73 & 89.78 & **78.69** & 64.84 & 71.08 \\ TriMF [22] & **90.26** & 90.34 & 90.30 & 73.01 & 71.63 & 72.35 \\ ER-LAC & 87.81 & 88.80 & 88.29 & 75.72 & **74.91** & **75.13** \\ ER-LAC * & 89.93 & **91.01** & **90.47** & 74.57 & 71.56 & 73.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the proposed model and other SOTA models on the CoNLL04 dataset. The bolded portion indicates the best result for each column.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**ADE**} \\ \cline{2-7}  & \multicolumn{3}{c}{**NER (\%)**} & \multicolumn{3}{c}{**RE (\%)**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline Relation-Metric [31] & 86.16 & 88.08 & 87.11 & 77.36 & 77.25 & 77.29 \\ CLDR+CLNER [32] & - & - & 88.3 & - & - & 79.97 \\ SpERT [14] & 88.99 & 89.59 & 89.28 & 77.77 & 79.96 & 78.84 \\ Table-Sequence [33] & - & - & 89.70 & - & - & 80.10 \\ CMAN [34] & - & - & 89.40 & - & - & 81.14 \\ REBEL [35] & - & - & - & - & - & 82.20 \\ Deeper [30] & 89.06 & 89.63 & 89.48 & 80.51 & 86.81 & 83.74 \\ PFN(ALBERT) [36] & - & - & 91.50 & - & - & 83.90 \\ ER-LAC & **91.67** & **93.03** & **92.34** & **83.07** & **86.74** & **84.86** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the proposed model and other SOTA models on the ADE dataset. All the metrics use macro-averaged values. The bolded portion indicates the best result for each column.  
the time taken to compute each sample on average during the training process, and the inference time is the time taken to compute each sample on average during the inference process. The results show that the proposed model has increased the parameter size by 9.27% and added less than 5% of the training time and inference time compared to the baseline model, which indicates that the increase in computational cost is minimal and the proposed model still has lower computational consumption, ensuring efficient usage in practical applications.