## 4 Experiment and Result Analysis
### Comparison of Results on Datasets  
We compare EINET with advanced models on three publicly available datasets. In order to fairly and comprehensively illustrate the effectiveness of models, three evaluation indicators are used: Precision, Recall, and F1. Among them, F1 is the most important evaluation index. For consistency with other models, micro- and macro-average results are compared on Conll04, micro-average results on SciERC, and macro-average results on ADE. **Conll04**: We report the average over three runs for the Conll04 dataset. The comparison of methods on the Conll04 dataset is shown in Table 1, where \(\dagger\) represents the calculation result of micro-average, \(\ddagger\) represents the result of macro-average calculation, and \(\ast\) represents that the model does not specify the calculation method. Compared with the baseline model SpERT, the F1 of EINET on the entity recognition exceeds 2.37% (micro-average), 2.26% (macro -average), and the F1 on the relation extraction exceeds 3.43% (micro-average), 3.04% (macro-average). EINET is also better than the current advanced model proposed by [PERSON] et al. [36]. The experimental results show that the EINET has achieved relatively advanced experimental results on Conll04 dataset, and the comparison with the baseline model shows that enhanced semantic information can indeed bring obvious benefits. **SciERC**: We report the average over three runs for the SciERC dataset. The comparison of methods on the SciERC dataset is shown in Table 2. Compared with the baseline model SpERT, the F1 of EINET on entity recognition is 1.01% higher, and the F1 on relation extraction is 2.09% higher. It is worth noting that, in terms of NER, the performance of EINET is better than the advanced model PL-Marker [37]. The experimental results show that EINET has reached a relatively advanced level on the SciERC dataset. Our proposed method of enriching semantics from explicit and implicit perspectives has a significant positive impact on scientifically relevant dataset. **ADE**: The comparison between the proposed model EINET and other methods on the ADE dataset is shown in Table 3. For a fair comparison with existing methods, the final results are averaged over 10 cross-validations. It is worth noting that ADE also contains 120 instances of relations with overlapping entities, which can be discovered by span-based approaches such as EINET and SpERT. These have been filtered in sequence tagging based work [32; 34; 38]. Compared with the baseline model SpERT, the F1 of EINET is 1.32% \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Entity**} & \multicolumn{3}{c}{**Relation**} \\
**Model** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline Relation-Metric [32]\(\ddagger\) & 84.46 & 84.67 & 84.57 & 67.97 & 58.18 & 62.68 \\ Biaffine Attention [33]\(\ddagger\) & - & - & 86.20 & - & - & 64.40 \\ Multi-turn QA [18]\(\dagger\) & 89.00 & 86.60 & 87.80 & 69.20 & 68.20 & 68.90 \\ Multi-head + AT [16]\(\ddagger\) & - & - & 83.61 & - & - & 61.95 \\ Multi-head [34]\(\ddagger\) & 83.75 & 84.06 & 83.90 & 63.75 & 60.43 & 62.04 \\ Hierarchical Attention [35]\(\ast\) & - & - & 86.51 & - & - & 63.32 \\ SpERT [26]\(\dagger\) & 88.25 & 89.64 & 88.94 & 73.04 & 70.00 & 71.47 \\ SpERT [26]\(\ddagger\) & 85.78 & 86.84 & 86.25 & 74.75 & 71.52 & 72.87 \\ MRC4 ERE++ [19]\(\ast\) & 89.30 & 88.50 & 88.90 & 72.20 & 71.50 & 71.90 \\ UMT w/ NLGQ [20]\(\ast\) & 88.70 & 88.80 & 88.80 & 72.90 & 71.60 & 72.20 \\ UMT w/ PseudoGQ [20]\(\ast\) & 88.80 & 89.00 & 88.90 & 73.20 & 71.60 & 72.40 \\ TriMF [28]\(\dagger\) & 89.26 & 90.34 & 90.30 & 73.01 & 71.63 & 72.35 \\ Two are better than one [36]\(\ddagger\) & - & - & 90.10 & - & - & 73.80 \\ Two are better than one [36]\(\ddagger\) & - & - & 86.90 & - & - & 75.80 \\ \hline EINET \(\dagger\) & 92.43 & 90.22 & **91.31** & 77.15 & 72.78 & **74.90** \\ EINET \(\ddagger\) & 90.65 & 86.70 & **88.51** & 77.98 & 74.16 & **75.91** \\ \hline \hline \end{tabular}
\end{table}