文献类型：期刊文献

英文题名：HIGR: Hierarchical Iterative Graph Reasoner forDocument-Level Event Causality Identification

作者：Ni, Jianwei[1]; Guo, Yi[1]; Fu, Jiaojiao[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2026

卷号：16598 LNAI

起止页码：210

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20262420921450)

语种：英文

外文关键词：Natural language processing systems - Neural networks

摘要：Event Causality Identification aims to detect causal relations between events in text, which depend on both local cues and global discourse structure. Existing document-level approaches typically perform uniform reasoning over all event pairs, overlooking two key facts: (1) intra-sentence causal relations are easier to recognize due to explicit local markers, and (2) confidently identified causality can provide structural information that benefits subsequent predictions. We propose the Hierarchical Iterative Graph Reasoner (HIGR), which explicitly leverages these observations. Within each iteration, HIGR first resolves intra-sentence causality to establish reliable local causal contexts and then uses them to support inter-sentence reasoning. Across iterations, HIGR progressively refines event representations by aggregating the causal structures identified so far, enabling increasingly informed predictions. We also introduce adaptive aggregation mechanisms that regulate information flow at both the stage and edge levels. Experiments show HIGR outperforms state-of-the-art methods on two datasets, with particularly significant improvements on challenging inter-sentence relations. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.