Abstract: Event causality identification(ECI) is a crucial research direction in the field of natural language processing,with the objective of accurately identifying whether the causal relations exists between two specific events.Current mainstream methods often utilize pre-trained language models to extract limited contextual semantic information from text to judge causal relationships.However,such methods tend to simplify the understanding of key event structures and their contextual semantics,failing to fully leverage the capabilities of pre-trained language models.Additionally,they overlook the significant role of historical events and relevant labels in constructing analogical reasoning to establish causal relations between target events.To address these challenges,model based on a prompt learning and hypergraph enhanced model(PLHGE) is proposed.The proposed model effectively captures global interaction patterns among events and the structural-semantic connections between current and historical events.By integrating descriptive knowledge with textual semantics,the model generates a hierarchical event structure.Additionally,PLHGE constructs a knowledge-based hypergraph to incorporate fine-grained and document-level semantic information,thereby enhancing its identification ability.Furthermore,a relationship-based knowledge prompt learning module is introduced to utilize latent causal knowledge within pre-trained language models to improve event causal relationship recognition.Finally,extensive experiments conduct on two public benchmark datasets,and the results demonstrate that PLHGE model outperforms existing baselines in the ECI task.

Key words: Event causality identification, Natural language processing, Prompt learning, Hypergraph