## 4 Experiments
### Branch Reasonableness Evaluation  
To improve the consistency of the identified events, we group them into branches based on a cohesive theme that connects the events within each branch. The logical grouping of interrelated events under a thematic node plays a significant role in the overall rationality of the event tree structure. To evaluate the rationality of each branch, we use clustering evaluation metrics such as the silhouette coefficient (SC) [48], Davies-Bouldin index (DB) [49], and Calinski-Harabasz index (CH) [50]. We adopt the above three metrics to evaluate the relevant event clustering performance of LSH-GEFR. Table 3 shows the performance of different methods on the interview data and Internet data. Tables 4 and 5 show the performance of different methods on the Twitter dataset and the news of the CNN dataset. It can be seen from the experimental results that the LSH-GEFR has achieved the best performance. We can also find that, upon removing the bilayer graph or the D2E-EC structure, the clustering performance decreases. Whether events are distributed under appropriate themes will affect the rationality of the event tree structure. Story Forest employs a bilayer approach to event recognition. The first layer is based on identifying keywords within the event documents, which are then grouped into clusters based on their semantic similarity. Subsequently, a document graph is constructed based on the keyword clusters, and an SVM classifier is trained to identify events with similar semantics and relevance. Similarly, EventKG utilizes an entity distance-based approach for event clustering, followed by training an SVM classifier to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Interview Data** & **Internet Data** & **Twitter Data** & **CNN Data** \\ \hline LDA + TO & 39.87\% & 44.26\% & 54.09\% & 53.72\% \\ Story Forest & 54.42\% & 57.62\% & 62.07\% & 61.90\% \\ EventKG & 58.87\% & 60.93\% & 68.07\% & 67.61\% \\ EventNET & 63.26\% & 65.46\% & 71.86\% & 70.45\% \\ LSH-GEFR & 70.14\% & 73.27\% & 79.75\% & 78.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results for path consistency. determine whether a given entity and its knowledge relation belong to the same class. EventNET, on the other hand, focuses on improving event feature extraction and clustering functions to enhance clustering performance. These methods are more effective than the simple LDA approach. However, these methods do not consider that events have certain relationships with each other, which can affect their thematic relevance. Additionally, there is a significant difference in the number of events under each theme, resulting in an uneven distribution. The LSH-GEFR algorithm proposed in this paper first utilizes the bilayer graph to capture potential connections between events and optimize event feature representation. Subsequently, the D2E-EC framework is employed to complete the event clustering task. DBSCAN is a density-based clustering method that performs well when the number of events is unevenly distributed under each theme. Furthermore, most clustering methods do not perform well on high-dimensional features; hence, we utilized DBN to compress the features and reduce them to low-dimensional representations that can still represent the original features. Clustering with these compressed features leads to significant improvements in clustering performance. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Source** & \multicolumn{4}{c}{**Twitter Data**} \\
**Method** & **SC** & **DB** & **CH** \\ \hline LDA + TO & 0.4377 & 0.3537 & 3.3915 \\ Story Forest & 0.5276 & 0.3112 & 4.2580 \\ EventKG & 0.5540 & 0.3273 & 3.8780 \\ EventNET & 0.5559 & 0.3132 & 3.7226 \\ LSH-GEFR (No GE) & 0.5458 & 0.3346 & 4.5008 \\ LSH-GEFR (No D2E-EC) & 0.5392 & 0.3177 & 3.8639 \\ LSH-GEFR & 0.5937 & 0.2911 & 4.8479 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing different methods on Twitter dataset.