Document-level Event Extraction Model Based on Span and Graph Convolutional Network
LIAO Tao
NIU Bingyu
Abstract：In order to solve the problem of existing event extraction methods in entity extraction subtasks,which made it difficult to fully utilize contextual information and led to low event extraction accuracy,the document-level event extraction based on span and graph convolutional network(DEESG)model was proposed.Firstly,an intermediate linear layer was designed to linearly process the encoded vectors,and annotation information was combined to calculate the optimal span.By improving the accuracy of determining the start and end positions of the span,the precision of entity extraction was enhanced.Secondly,a method for constructing heterogeneous graphs was designed,using a pooling strategy to represent entities and sentences as nodes of the graph.Heterogeneous graphs were constructed based on the proposed edge-building rules to establish global information interaction.Multi-layer graph convolutional networks(GCN)were used to convolve heterogeneous graphs,obtaining entity and sentence representations with contextual information,thus solving the problem of insufficient utilization of contextual information.Thirdly,a multi-head attention mechanism was used to detect event types.Finally,argument roles were assigned to the entities in the combination to complete the event extraction task.Experiments were conducted on the Chinese financial announcements(ChFinAnn)dataset,and the results showed that DEESG model improved the F1 score by 1.3 percentage points compared to graph-based interaction model with a tracker(GIT).The research confirmed that the DEESG model could be effectively applied in the field of document-level event extraction.
Keywords：event extractionspanentity extractionheterogeneous graphgraph convolutional networkcontext information
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 108-113 )