# An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 Ablation Experiments  
(1) Influence of different factors on model performance. In order to analyze the influence of each part of the method and evaluate the contribution of different factors on the model's performance, further ablation experiments were conducted on the dataset to gradually eliminate the following four modules to observe the performance changes, namely the Bi-LSTM final layer, the Bi-LSTM intermediate layer, the DAGCN layer, and the gating mechanism. In particular, since the gating mechanism is bidirectionally weighted, if the Bi-LSTM final layer module is removed, the model only has  
Figure 5: \(F_{1}\) values of the different models in the datasets [2; 3; 14; 15; 16]. a unidirectional input, so the ablation of this part is not gated by default. For the ablation of the DAGCN layer, considering the difference between the features of the BERT output to the middle and final layers, gating can also effectively control the flow of information; therefore, we chose to use a gated dynamic fusion. We used a concatenation layer for feature fusion when the gating mechanisms were eliminated. Table 3 lists the results. It can be seen from the experimental results that different results are produced by eliminating different layers in the open-domain event extraction task. It was found that the DAGCN layer has a great impact on model performance, while the Bi-LSTM final layer and the gating mechanism have a lesser impact, and the Bi-LSTM intermediate layer has the least influence. Removing the DAGCN layer reduced the model performance by 0.8, again providing reliable validation of the semantically enhanced dependency syntax approach. The elimination of the Bi-LSTM final layer resulted in a 0.6 decrease in model performance, indicating that the Bi-LSTM final layer and gating mechanism have certain effects on the model, but the convolution of the dependency attention diagram is not as important for feature modeling. Since the elimination of the Bi-LSTM final layer does not carry the gating mechanism by default, we ablate the gating mechanism alone, and the experimental results show that there is no significant difference between the model \(F_{1}\) values and the elimination of the Bi-LSTM final layer, further demonstrating the effectiveness of the gating mechanism, or, in other words, the necessity of feature fusion. At the same time, if the Bi-LSTM intermediate layer factor is eliminated, the performance of the model decreases to 52.7, which is relatively mild. This indicates that the middle layer has little effect on the performance of the model. This may be because the feature representation provided by some intermediate layers is similar to that of the final layer, which has a lesser impact on the model performance, while the gating mechanism can dynamically integrate the semantic and dependency syntactic information to provide a richer representation. The impact of intermediate layer selection on model performance. In addition, different to the study of [PERSON] et al. [16], this paper conducted an experiment on the open-domain event extraction task. Considering that the data distribution and feature methods of different sub-tasks are different and that the selection of the middle layer will affect the performance of the model, we aimed to select the optimal result to improve the extraction performance. Therefore, reference experiments were carried out to select different intermediate layers. From layers 1 (the final layer), 3, and 4, we selected intervals of 4, 3, and 2, respectively, to reduce. The experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Scheme Matching (\%)**} \\ \cline{2-4}  & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F_{1}}\) \\ \hline \(-\) Bi-LSTM Final Layer & 52.9 & 51.7 & 52.3 \\ \hline \(-\)Bi-LSTM Middle Layer & 53.4 & 52.0 & 52.7 \\ \hline \(-\)DAGCN & 53.0 & 51.2 & 52.1 \\ \hline \(-\)Gating Mechanism & 53.1 & 51.7 & 52.4 \\ \hline \hline \end{tabular}
\end{table}