# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
###### Abstract  
The extant event detection models, which rely on dependency parsing, have exhibited commendable efficacy. However, for some long sentences with more words, the results of dependency parsing are more complex, because each word corresponds to a directed edge with a dependency parsing label. These edges do not all provide guidance for the event detection model, and the accuracy of dependency parsing tools decreases with the increase in sentence length, resulting in error propagation. To solve these problems, we developed an event detection model that uses a self-constructed dependency and graph convolution network. First, we statistically analyzed the ACE2005 corpus to prune the dependency parsing tree, and combined the named entity features in the sentence to generate an undirected graph. Second, we implemented an enhanced graph convolution network using the multi-head attention mechanism to understand the representation of nodes in the graph. Finally, a gating mechanism combined the semantic and structural dependency information of the sentence, enabling us to accomplish the event detection task. A series of experiments conducted on the ACE2005 corpus demonstrates that the proposed method enhances the performance of the event detection model. event detection; dependency parsing; graph convolution network; attention mechanism; natural language processing +
Footnote †: journal: Medical  
words in the dependency relationship, whereas the latter describes the specific type of dependency relationship between two words, reflecting the differences in the syntactic characteristics between different words. Graph Convolution Network (GCN) [4] is a deep-learning method used for processing and analyzing graph data, which can learn the results of dependency parsing. By employing a convolution operation on graph data, a fresh representation of nodes is produced by consolidating the features of both nodes and their neighbors. To date, researchers have shown a keen interest in GCNs, and methods grounded in GCN have become some of the leading approaches in event detection tasks. However, [PERSON] et al. [5] and [PERSON] et al. [6] found that using a large number of edge types derived from dependency parsing substantially increase the number of model parameters and requires many training calculations. [PERSON] et al. [7] stated that \"nsubj\" (nominal subject), \"dobj\" (direct object) and \"nmod\" (noun compound modifier) constituted 32.2% of the dependency labels related to trigger words in the ACE2005 dataset (each relationship accounted for 2.5% of an average total of 40 dependencies). Although dependency parsing can provide useful structural information for event detection tasks, large amounts of redundant information are introduced and the effect of event detection is reduced. To solve the above problems, we developed an event detection model using a self-constructed dependency and graph convolution network. First, we calculated the data statistics on the dependency parsing of the ACE2005 dataset; based on the results, we pruned the dependency parsing tree of a sentence and combined the named entity features to build an undirected graph. Second, we used the improved graph convolution network based on the multi-head attention mechanism to capture graph nodes. The representation of nodes in the graph was dynamically updated during the whole training process. The importance of dependency was distinguished through the multi-head attention mechanism. Finally, we used a gating mechanism to dynamically combine the semantic information of the sentence with the structural dependency information of the event. The main contributions of this study are as follows:  
* We designed a novel graph construction method by pruning the dependency parsing tree and combining the named entity features. Figure 1: Dependency parsing results of the sentence “During a meeting, Team owner told [PERSON] that his service were no longer necessary tomorrow”.