Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 156-167.doi: 10.11896/jsjkx.250500006

• Artificial Intelligence • Previous Articles     Next Articles

Document-level Event Argument Extraction Model Based on Hierarchical Dependency Aggregation and Event Enhancement

DING Zhijun1, AI Fangju1,2,3, LIU Aihuan4   

  1. 1 School of Computer Science,Hubei University,Wuhan 430062,China
    2 Hubei Key Laboratory of Big Data Intelligent Analysis and Application(Hubei University),Wuhan 430062,China
    3 Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence,Wuhan 430062,China
    4 School of Computer Science,University of Science Malaysia,Penang 11800,Malaysia
  • Received:2025-05-06 Revised:2025-08-12 Online:2026-07-15 Published:2026-07-10
  • About author:DING Zhijun,born in 1999,postgra-duate,is a member of CCF(No.A00667G).His main research interests include natural language processing and event argument extraction.
    AI Fangju,born in 1973,Ph.D,associate professor.Her main research interests include natural language processing and data mining.
  • Supported by:
    Young Program of the Natural Science Foundation of Hubei Province,China(2023AFB313).

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Abstract: In the task of document-level event argument extraction,existing prompt-based learning models face challenges in comprehensively capturing the holistic semantic understanding of documents and neglect the interaction between documents and prompt templates.To address these limitations,a novel document-level event argument extraction model is proposed,incorporating hierarchical dependency aggregation and an event enhancement mechanism.Initially,the vector representations of the input sequence are obtained through BART.Subsequently,a hierarchical dependency aggregation architecture is introduced,consisting of a semantic hierarchical dependency-aware mechanism and a structural hierarchical dependency-aware mechanism,designed to capture semantic correlations and structural information among entity mentions respectively.These features are dynamically fused via a self-attention mechanism.The integrated relation matrix is then leveraged to guide the aggregation of contextual information from relational dependency graphs onto corresponding entity mentions,thereby updating mention representations.These enhanced representations are subsequently incorporated into the original input representations to strengthen the model's capability in perceiving entity relations and facilitating document-prompt template interactions.Finally,an event enhancement mechanism is implemented to prioritize contextually relevant information for the current extraction task while mitigating information redundancy and interference,ultimately improving argument extraction accuracy.Experimental results conducted on RAMS and WikiEvent two public dataset demonstrate that the proposed model achieves superior F1 scores compared to baseline models,with significant improvements in comprehensive performance metrics.

Key words: Document-level event argument extraction, Prompt learning, Hierarchical dependency aggregation, Self-attention mechanism, Event enhancement mechanism

CLC Number: 

  • TP391
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