## 4 Experiment and Result Analysis
### Ablation Analysis  
In this section, we investigate the effectiveness of each module in the proposed EINET. The results of the ablation experiment are detailed in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**Entity(F1)**} & \multicolumn{3}{c}{**Relation(F1)**} \\
**Model** & **Micro-Average** & **Macro-Average** & **Micro-Average** & **Macro-Average** \\ \hline
1 & EINET & 91.31 & 88.51 & 74.90 & 75.91 \\
2 & w/o SRL & 89.93 & 87.57 & 72.86 & 73.50 \\
3 & w/o Bi \(-\)LSTM\({}_{e}\) & 90.39 & 87.50 & 73.73 & 74.62 \\
4 & w/o Bi \(-\)LSTM\({}_{r}\) & 90.72 & 88.01 & 73.46 & 74.63 \\
5 & w/o Bi \(-\)LSTM\({}_{e}\) and Bi \(-\)LSTM\({}_{r}\) & 90.19 & 87.33 & 73.16 & 74.39 \\
6 & w/o global semantics (relation) & 91.01 & 88.10 & 74.26 & 75.78 \\
7 & w/o local context length information & 90.85 & 87.87 & 74.07 & 75.52 \\
8 & w/o global semantics (relation) and local & 90.40 & 87.40 & 73.56 & 74.71 \\
9 & Baseline & 88.94 & 86.25 & 71.47 & 72.87 \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation study on Conll04 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Entity**} & \multicolumn{3}{c}{**Relation**} \\
**Model** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline SciIE [17] & 67.20 & 61.50 & 64.20 & 47.60 & 33.50 & 39.30 \\ DyGIE [22] & - & - & 65.20 & - & - & 41.46 \\ DyGIE++ [25] & - & - & 67.50 & - & - & 48.40 \\ SpERT [26] & 70.87 & 69.79 & 70.33 & 53.40 & 48.54 & 50.84 \\ UNIRE [39] & 65.80 & 71.10 & 68.40 & 37.30 & 36.60 & 36.90 \\ PFN [40] & - & - & 66.80 & - & - & 38.40 \\ PURE [41] & - & - & 68.90 & - & - & 50.10 \\ TriMF [28] & 70.18 & 70.17 & 70.17 & 52.63 & 52.32 & 52.44 \\ SpERT.PL [43] & 69.82 & 71.25 & 70.53 & 51.94 & 50.62 & 51.25 \\ PL-Marker [37] & - & - & 69.90 & - & - & **53.20** \\ \hline EINET & 71.26 & 71.43 & **71.34** & 55.34 & 50.73 & 52.93 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance on SciERC dataset. (The highest F1 is shown in bold.) \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Entity**} & \multicolumn{3}{c}{**Relation**} \\
**Model** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline BiLSTM + SDP [38] & 82.70 & 86.70 & 84.60 & 67.50 & 75.80 & 71.40 \\ Multi-head [34] & 84.72 & 88.16 & 86.40 & 72.10 & 77.24 & 74.58 \\ Multi-head+AT [16] & - & - & 86.73 & - & - & 75.52 \\ Relation-Metric [32] & 86.16 & 88.08 & 87.11 & 77.36 & 77.25 & 77.29 \\ SpERT(without overlap) [26] & 89.26 & 89.26 & 89.25 & 78.09 & 80.43 & 79.24 \\ SpERT(with overlap) [26] & 88.99 & 89.59 & 89.28 & 77.77 & 79.96 & 78.84 \\ PFN [40] & - & - & 89.60 & - & - & 80.00 \\ Two are better than one [36] & - & - & 89.70 & - & - & 80.10 \\ \hline EINET(without overlap) & 90.03 & 91.12 & **90.57** & 80.38 & 83.79 & **82.04** \\ EINET(with overlap) & 89.69 & 91.29 & **90.48** & 79.74 & 83.11 & **81.38** \\ \hline \end{tabular}
\end{table} 
Table 3: Performance on ADE dataset. (The highest F1 is shown in bold. )To demonstrate the effectiveness of explicit semantic information, we design Model 2 and compare it with the complete model EINET (Model 1). Model 2 is EINET with removal of the vector representation of SRL. Compared with EINET, the F1 of Model 2 in name entity recognition decreased by 1.38% (micro-average) and 0.94% (macro-average), and it is decreased by 2.04% (micro-average) and 2.41% (macro-average) in relation extraction. The obvious drop in model performance after removing the explicit semantic information provided by SRL demonstrates the effectiveness of explicit semantic information. Since the explicit semantic information is introduced in the word representation, removing SRL has a significant impact on both named entity recognition and relation extraction. To demonstrate the effectiveness of enhancing implicit semantic information, we compare Model 3, Model 4, and Model 5 with the complete model EINET. After removing the named entity recognition Bi-LSTM (\(\text{Bi}-\text{LSTM}_{e}\)), we obtain Model 3.