# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 3 Methods
### Dependency Graph Construction Method  
We used the Stanford CoreNLP tool [3] to analyze the dependency syntax of sentences, and we statistically analyzed the processing results of all sentences in the ACE2005 corpus.  
Figure 2: Model graph based on proposed self-constructed dependency graph and graph convolution neural network.  
According to Table 1, the probability of \"det\" (determiner) and \"case\" (case marking), which appear most frequently in all dependent syntactic label as the trigger word related label, is lower than 10%. We considered that these types of edges can be classified as redundant information. Therefore, based on the above statistics, we proposed a pruning dependency syntax tree. For each branch, if the current parsing label does not belong to the top 10 as the probability of label associated with trigger words, it is removed. The pruning result is shown in Figure 3.  
Because the input of GCN is an undirected graph, the dependency relationship is built based on the following rules:  
1. Reverse edges (E(\(w_{j},w_{i}\))): To strengthen the flow of dependency information in the opposite direction on the edge of the graph, and better capture the relationship between entities, we add the corresponding reverse edge E(\(w_{j},w_{i}\)) for each edge E(\(w_{j},w_{i}\)) generated by dependency parsing.
2. Sequence edges (E(\(w_{i},w_{i+1}\))): To restore the integrity of the sentence and capture the adjacency relationship in time sequence, we add an adjacency edge E(\(w_{i},w_{i+1}\)) to each adjacency word.
3. Entity edges (E(\(w_{e1},w_{e2}\))): To enhance the model's understanding of the entity features in the sentence, the named entity recognition method is used to extract the entities in the sentence and connect them with each other.
4. Self-connected edges (E(\(w_{i},w_{i}\))): We add self-connected edges to each character to prevent nodes from ignoring their own characteristics.  
To extract the entities in the sentence, we use the sequence labeling method based on the Begin, Inside, Other (BIO) pattern; we use conditional random field (CRF) to identify the entities. In the training phase, we minimized the following loss:  
\[\text{L}_{\textit{iter}}=-\sum_{s\in\text{D}}\log P(y_{s}\mid s) \tag{2}\]  
where \(y_{s}\) is the golden label of \(s\). For inference, we used the Viterbi algorithm to decode the label sequence with the maximum probability.  
The final result of the self-constructed dependency graph is shown in Figure 4 (self-connected edge omitted); the nodes with the darkened background in the figure indicate the entities that we extracted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dependency Parsing Labels**} & \multirow{2}{*}{**Percentage of Labels in the Corpus (\%)**} & \multicolumn{2}{c}{**Probability of Label Associated**} & \multirow{2}{*}{**Ranking**} \\  & & **with Trigger Words (\%)** & \\ \hline nsubj:pass (nominal subject) & 1.01 & 31.66 & 1 \\ auxcpass (auxiliary) & 1.18 & 27.96 & 2 \\ obl (oblique nominal) & 5.38 & 27.78 & 3 \\ advcl (adverbial clause modifier) & 1.34 & 27.76 & 4 \\ det (determiner) & 8.81 & 8.98 & 26 \\ case (case marking) & 11.11 & 7.82 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of dependency parsing tags related to trigger words on ACE2005 dataset.  
Figure 3: The result of pruning dependency syntax tree analysis.