Abstract：Focusing on improving the evolutionary graph construction and enriching the event representation, this paper proposes an event prediction model based on the event evolutionary graph and Graph Convolutional Network(GCN). This model applies an event extraction model, and redefines the edge’s weight on the event evolutionary graph by combining frequency and mutual information. The representation of the event context is learned by BiLSTM and memory network, which is fed as the input into GCN under the guidance of the event evolutionary graph. The final event prediction is jointly completed by such event-relationship aware, context-aware, and neighbor aware event embeddings. Experiment results on the Gigaword benchmark show that the proposed model outperforms six advanced models in event prediction accuracy, with 5.55% increase compared with the latest SGNN method.
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2022, Vol. 36 
