# Similarity Calculation via Passage-Level Event Connection Graph
## 5 Experiments and Analyses
### Experimental Results
#### 5.2.2 Comparison of Different Event Extraction Methods  
Our similarity calculation needs to construct an event connection graph to reveal the core event to calculate text similarity. In our paper, this graph is constructed by the  
Figure 5: \(F1\) values when we change the threshold from 0.1 to 1.0. The threshold is utilized to decide whether to connect two triggers in the event connection graph or not. sentence-level event extraction method shown in [40]. We note this method as OneIE as it is called in [40]. There raises a doubt that whether different event extraction methods affect the final calculating results or not. Therefore, we design an experiment to see the similarity calculation results with event connection graphs constructed by different event extraction methods across all the testing corpora. The following table just illustrates the performances of our calculation on the condition that the event connection graph is constructed via several popular sentence-level event extraction methods. The event connection graph includes both trigger and argument; thus, the chosen event extraction methods should jointly extract trigger and argument meanwhile. We choose BeemSearch [53], JointTransition [54], and ODEE-FER [55] as baselines. BeemSearch is one of the classic joint event extraction methods, which encodes text via one-hot feature and applies local and global features to label trigger and argument meanwhile. JointTransition and ODEE-FER are both based on neuron model. The significant difference between them is that JointTransition applies transition model to characterize the relation between trigger and argument, while ODEE-FER integrates latent variable into neuron model to extract open-domain event free from event schema predefinition. Multi-task learning framework is utilized in ODEE-FER to identify trigger and argument concurrently. The corpora for paraphrase, query match, and manually annotated are abbreviated as Para, Q&Q, and MA, which are also used in the following tables. As shown in Table 2, it can be found that different event extraction methods do not affect the performances of our calculation much. This situation is due to the following two reasons. First of all, the construction of event connection graph is only the first step in our calculation. The extracted triggers and arguments are subsequently measured to indicate their importance via centrality measurement. During the measuring process, the incorrectly extracted triggers and arguments can be eliminated. In Table 2, we also add an extreme case, noted as Extreme listed in the last row, where we treat a verb in the sentence as trigger and noun as argument. If there is more than one trigger in the sentence, we construct polygon for each trigger following the approach shown in Section 3. It can be observed that the result obtained from Extreme is a little different from the ones obtained from the other methods. That indicates we do not need the precise event extraction results, as long as the extracted results contain enough triggers and arguments. Furthermore, two improvements proposed in Section 4 can also help remove the adverse effect brought from the incorrectly extracted triggers and arguments. In detail, one improvement tunes the vector representation of the trigger, and links the semantically similar triggers in the event connection graph. Regarding the triggers incorrectly extracted, they are little related to the core event mentioned in the text. Thus, these triggers do not locate at the center of the event connection graph. After the measuring process, these triggers will be valued with little weights. They will not be chosen as the core event to measure text similarity. The other improvement is to integrate the information carried by the entire text into the chosen nodes via graph embedding. In some cases, even if the incorrectly extracted triggers and arguments are chosen as the core event, after graph embedding, the information carried by the entire text can be integrated into the incorrectly extracted triggers and arguments.