# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 2 Related Work  
Modeling event extraction into classification tasks based on machine learning is the current mainstream research direction; deep learning and neural networks have become the main means of event extraction. Fully connected neural networks and circular neural networks have been successfully applied to event extraction tasks. The attention mechanism and self-attention mechanism have also been introduced to the event extraction task because of their ability to capture links in the text context. In the research on event extraction for English text, [PERSON] et al. [8] proposed a classic model: they used a dynamic multi-pooled convolutional neural network and the location information provided by trigger words to capture the context information of an entity. [PERSON] et al. [9] abandoned the pipeline extraction method on the basis of [PERSON] et al., introduced a joint extraction method into event extraction, and extracted the semantic features in sentences using bidirectional LSTM [10], which they fused with the sentence structure features. [PERSON] et al. [11] and [PERSON] et al. [12] introduced many word embedding methods, including word embedding, position embedding, type embedding and dictionary embedding and used sentence semantics. To further integrate syntactic information into the model, [PERSON] et al. [13] introduced a syntactic dependency tree into the model, and feature aggregation was performed by syntactic dependency through RNN [14]. Finally the final extraction result was obtained by aggregating the relationship pairs between entities.  
After Google proposed the Transformer architecture [15] in 2019, using the self-attention mechanism to extract context in the word vector, on the basis of Transformer, the Bert architecture was proposed to achieve unsupervised training on massive texts. [PERSON] et al. [16] then introduced the Bert architecture into event extraction, and used it as a generation word vector for the next feature aggregation, and they proposed dataset generation technology based on Bert's mask mechanism. [PERSON] et al. [17] introduced an event extraction mechanism based on the Bert mechanism into the key event extraction for biomedicine and optimized it for relevant fields. To address the challenges posed by complex syntax, many graph-based models have been introduced to the event extraction model. [PERSON] et al. [5] constructed isomorphic graph structures by combining the results of syntactic dependency analysis with words; they used graph attention networks for feature aggregation and also simplified and optimized the complexity of graphs. In addition to sentence-level event extraction, corresponding studies have been conducted on document-level event extraction. [PERSON] et al. [18] proposed an end-to-end document-level event extraction system in the field of Chinese finance. [PERSON] et al. [19] proposed filtering out key sentences from the document level and then performing the sentence-level event extraction task, to achieve text-level event extraction. [PERSON] et al. [20] proposed the idea of multi-hop dependency parsing, using the graph attention network (GAT) [21] to transform the dependency syntax tree into a multi-level graph structure for modeling, thus enhancing the semantic association between trigger words and related entities. [PERSON] et al. [7] introduced dependency label information into GCN and proposed an edge-enhanced graph neural network model, which achieved better performance than the current benchmark model.  
However, because the focus of the model is integrating syntactic structure and typed dependency labels, it only uses a simple splicing method to fuse the semantic information and structural dependency information of sentences, without distinguishing the importance of dependency relationships. Effectively using the results of sentence dependency parsing and integrating this structured information into the model without adding redundant information still remain challenging in this context.