# An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information
## 5 Conclusions  
In this paper, an improved open-domain event extraction method based on a dynamic fusion of semantic and dependency syntax is proposed for open-domain event extraction tasks. Based on a BERT fusion of semantic information and semantically enhanced dependency syntax information, rich feature representations were obtained and input into the neural topic model, solving the problem of existing open-domain event extraction methods, which lack semantic information and fail to capture long-range information. The improvement was made by the following aspects: Firstly, BERT final layer features were output through the Bi-LSTM to obtain a rich semantic representation. Secondly, to avoid semantic and syntactic interference, BERT middle layer features were obtained and input into the Bi-LSTM network. Then, DAGCN was used to increase nodes' attention to information and reduce dependency information noise. Thirdly, the gating mechanism was used to integrate semantic information and semantic-enhanced dependency syntactic information to reasonably control the feature flow to improve the accuracy of event extraction. The results of the main and ablation experiments on the dataset show that the improved method of fusing semantic and dependent syntactic information is very effective for the study of open-domain event extraction tasks. However, current open-domain extraction methods are inferior to traditional methods in terms of their extraction performance. This means that the proposed method has a lot of room for improvement, and more research and investigations are still needed to improve the accuracy and efficiency of open-domain event extraction. Possible future research directions can start with the following points. First, there should be a deeper understanding of context. The existing open-domain event extraction model still has a poor understanding of context and can only extract information relevant to the text surface. In the future, deeper contextual understanding techniques, such as consciousness and knowledge maps, should enable open-domain event extraction models to more accurately understand the text and extract event information. Second, multi-modal information should be integrated. Future open-domain event extraction models need to be able to process multi-modal information, such as pictures and videos. Since multi-modal information has \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spacing Number** & **Corresponding Layer** & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F_{1}}\) \\ \hline
5 & 1, 6, 11 & 51.6 & 50.0 & 50.8 \\ \hline
4 & 1, 5, 9, 11 & 52.5 & 51.7 & 52.1 \\ \hline
3 & 1, 4, 7, 10 & 53.6 & 52.2 & 52.9 \\ \hline
3 & 2, 5, 8, 11 & 52.5 & 52.3 & 52.4 \\ \hline
3 & 3, 6, 9, 12 & 51.8 & 51.4 & 51.6 \\ \hline
2 & 2, 4, 6, 8 & 50.9 & 51.5 & 51.2 \\ \hline
2 & 5, 7, 9, 11 & 51.2 & 50.6 & 50.9 \\ \hline
1 & 13 & 51.5 & 49.2 & 50.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effects of intermediate layer selection on model performance. a strong correlation with text, integrating multi-modal information and text information can enable a more comprehensive understanding of event information, which is of great significance for guiding information extraction in the future. Third, there is a need for more accurate terminology weighting methods. The parts of speech and semantic expression of different words in a sentence are different. In the process of event extraction, attention should be paid to the words that are most relevant to the semantic meaning of the sentence. Therefore, the weight allocation of different words is also very important and is conducive to improving the accuracy of event extraction. We believe that with the development of deep learning and knowledge-mapping technologies, open-domain event extraction techniques will gradually mature and our understanding of natural language models will become deeper and deeper. Conceptualization, L.H. and Q.Z. ; methodology, Q.Z. and H.W. ; software, Q.Z. ; formal analysis, Q.Z. ; investigation, Q.Z. ; resources, L.H.