# An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information
## 4 Experiments
### Experimental Results and Analysis  
#### 4.3.1 Comparison Experiments  
In order to verify the validity of the proposed model, the following models were selected for experiments on the dataset as the main experiment objects, which are mainly divided into three categories: the model based on semantic analysis, the model based on dependency parsing, and the model based on joint semantic syntax. Firstly, the Bi-LSTM model was selected based on semantic analysis. This model combines forward and backward LSTM, which can consider contextual information when processing sequence data and capture long-distance dependency characteristics. Secondly, DBRNN [14], GCN, GCN-ED [2], and DAGCN [3] were used to model dependency parsing. Specifically, based on Bi-LSTM, DBRNN integrates dependency syntactic information and adds a dependency bridge for event extraction tasks. However, the GCN modeling concatenates the word vector and entity-type vector as the input to the GCN to improve the event extraction performance. Since the GCN only considers the relationship between nodes, researchers proposed a variant of the GCN, the GCN-ED, based on this model. In addition to the embedding vector of nodes, the adjacency matrix was also used to calculate the embedding vector of the edge. Because it considers the relationship between nodes and edges, it can better capture complex relationships. In addition, the DAGCN enhances the differentiation of different features to obtain key information features. Finally, a model combining multiple neural network structures proposed by [15; 16] was selected. [PERSON] et al. [16] selected BERT intermediate layer features to reduce the interaction between the semantic and dependency syntax and improve the extraction accuracy, while GFDS [15] proposed a method to dynamically integrate semantic and dependency syntax information using a gating mechanism. The experimental results are shown in Table 2.  
As shown in Figure 5, in order to observe data indexes more directly, a line chart was used to compare the \(F_{1}\) values of several models in the experimental results. From the overall performance, it is obvious that the method of discovery has the best performance in \(F_{1}\), with P, R, and \(F1\) equal to 53.5, 52.3, and 52.9, respectively. Bi-LSTM showed the worst performance, with an \(F_{1}\) value of 49.3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Scheme Matching (\%)**} \\ \cline{2-4}  & \(P\) & \(R\) & \(F_{1}\) \\ \hline Bi-LSTM & 60.5 & 41.6 & 49.3 \\ \hline DBRNN [14] & 52.1 & 47.7 & 49.8 \\ \hline GCN-ED [2] & 53.1 & 49.8 & 51.4 \\ \hline DAGCN [3] & 53.5 & 50.4 & 51.9 \\ \hline [PERSON] et al. [16] & 52.6 & 49.7 & 51.1 \\ \hline GFDS [15] & 52.5 & 51.9 & 52.2 \\ \hline Our Model & 53.5 & 52.3 & 52.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment results. In addition, we found that in the dependency syntactic model, DAGCN had the best performance, with an \(F_{1}\) score of 51.9, and GCN-ED was second, both higher than DBRNN's \(F_{1}\) score of 49.8. This is because DBRNN only uses a dependency bridge to enhance the dependency syntactic information, mainly focusing on the grammatical information between words and ignoring information such as the modifier's phrase or verb voice, thus consuming too many computing resources for complex and long sentences. The use of GCN modeling can integrate various information modeling and capture more comprehensive language features. It can also adaptively learn different types of events in different contexts and complex language environments. However, using [PERSON] et al. 's combined method, the DAGCN's \(F_{1}\) score decreased by 0.8 compared with the optimal method, and there was no significant improvement compared with DBRNN. After further analysis, it is believed that GCNs are more suitable for complex and diverse open-domain event extraction tasks.