IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains - Hefei Chen; Yuanyuan Cai; Zexi Song; Yiyao Zhang; Hongbo Zhang (Applied Sciences, 2025)
Event Causality Identification (ECI) is a crucial task in Information Extraction (IE). However, information about events described in documents is often distributed across sentences, which makes it difficult for existing studies to capture long-distance causal relations between events. To address these issues, this paper proposes Iterative Event Causal Identification (IECI), a pipelined framework for event causality identification that integrates two modules. The first module introduces Prompt-Based Event Detection (PRED), which integrates semantic role awareness with prompt templates to provide foundational input for the next module. The second module proposes the Semantic-Role Guided Causal Inference Graph (SRCIG), which identifies causal relations between events by constructing a causal graph and applying a dynamic threshold adjustment mechanism during the iterative process. Our experiments show that PRED and IECI consistently outperform the state-of-the-art baseline model. Specifically, on the EventStoryLine dataset, they achieve F1 improvements of 3.7–9.8% and 4.2–18.8%, respectively, while on MAVEN-ERE the gains are 4.2–10.3% and 1.0–40.3%. This demonstrates the effectiveness and robustness of the proposed framework in both event detection and event causality identification.