# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 5 Conclusions  
This paper proposes a combined extraction model ESGM to solve nested entities and the relationship of overlapping problems. Different from the traditional span-based collaborative extraction model, this model combines a pointer network and one-to-many matching principle to predict the start and end of the span to extract nested entities and solve the redundant span problem. At the same time, in order to enhance the correlation  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **F1** & \(\Delta\) \\ \hline ESGM(ours) & 92.6 & - \\ -gate unit for object extraction & 85.7 & \(-6.9\) \\ -span match & 87.8 & \(-4.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets/Model**} & \multicolumn{4}{c}{**WebNLG**} & \multicolumn{4}{c}{**NYT**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline NovelTagging & 62.4 & 31.7 & 42.0 & 52.5 & 19.3 & 28.3 \\ CopyRe & 59.4 & 53.1 & 56.0 & 32.2 & 28.9 & 30.5 \\ GraphRel & 63.9 & 60.0 & 61.9 & 44.7 & 41.1 & 42.9 \\ CopyR & 77.9 & 67.2 & 72.1 & 63.3 & 59.9 & 61.6 \\ CasRel & 89.7 & 89.5 & 89.6 & 93.4 & 90.1 & 91.8 \\ TPLinker & 91.3 & 92.5 & 91.9 & 91.8 & 92.0 & 91.9 \\ CasDE & 90.2 & 90.9 & 90.5 & 90.3 & 91.5 & 90.9 \\ \hline ESGM (ours) & 91.8 & 92.6 & 92.2 & 93.6 & 91.7 & 92.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of our experiments on the datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**New\_Data**} \\ \cline{2-4}  & **Precision** & **Recall** & **F1** \\ \hline CasRel & 76.1 & 71.0 & 73.4 \\ ESGM(ours) & 80.5 & 77.0 & 79.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experiment results of ESGM model and CasRel Model on the new_data dataset.  
between modules, ESGM model adds a gating unit before the object extraction, fusing the subject information and the global information of the sentence together to improve the adaptability and accuracy of the model. The ESGM model in this paper has been tested on several data sets such as NTY and WebNLG to verify its superiority, and the results show that the precision and F1 value of the ESGM model are higher than that of the comparison model. However, the ESGM model dealing with document level and open domain relation extraction still needs to be improved, which will be our future research work.  
Conceptualization, N.Z. and J.X.; Methodology, N.Z. and J.X.; Software, N.Z. and J.X.; Validation, N.Z. and J.X.; Formal analysis, N.Z. and J.X.; Investigation, N.Z. and J.X.; Resources, N.Z., J.X. and Q.C.; Data curation, N.Z. and J.X.; Writing-original draft, N.Z. and J.X.; Writing.--review & editing, N.Z., J.X. and V.C.; Visualization, N.Z., J.X., Q.C. and V.C.; Supervision, N.Z., J.X., Q.C. and V.C.; Project administration, N.Z., J.X. and Q.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created.  
The authors declare no conflict of interest.