摘要： 事件抽取是信息抽取领域中的一个重要任务，其目标是从自然语言文本中识别并提取出特定的事件或事实信息。事件重叠的是事件抽取任务的一个关键问题，即同一个词语可能充当不同事件类型的触发词或者在不同事件中扮演不同的角色。目前的重叠事件抽取方法往往未能充分考虑论元角色之间的关联关系，这在一定程度上影响了事件抽取的效果。针对事件抽取领域的事件嵌套挑战，该研究提出了一种基于论元关联图的级联类型事件抽取模型模型—AAGCTEE（Cascading Type Event Extraction Model Based On Argument Association Graph）。AAGCTEE依托XLM-roberta-base实现深度编码，并通过条件层归一化及空洞卷积技术优化词对间的表征。在触发词识别模块中，AAGCTEE的级联类型预测器(Cascade Type Predictor, CTP)能够精准辨识多类事件相关的触发词，有效解决了传统事件抽取中常见的触发词嵌套难题。在论元识别分类模块中，AAGCTEE借助论元关联图(Argument Association Graph, AAG)和全局归一化解码策略(Global Normalization Decoding, GND)，高效处理了复杂的嵌套论元结构。 AAGCTEE的触发词识别、触发词分类、论元识别、论元分类四个指标的平均F1值在中文数据集DUEE、FewFC与英文数据集PHEE、CASIE上均表现出色，在四个数据集上分别比对比模型平均高9.67%、9.22%、18.95%和37.31%。与分别缺失级联类型预测器、论元关联图和的消融实验相比AAGCTEE在上述四个评价指标的平均F1得分上平均提高了6.92%、6.68%和6.45%，验证了AAGCTEE在提取复杂事件方面的有效性。

Abstract: Event extraction is a crucial task in the field of information extraction, aimed at identifying and extracting specific event or factual information from natural language text. A key challenge in event extraction is event overlap, where a single word may act as a trigger for different event types or play varying roles across events. Existing methods for overlapping event extraction often fail to adequately consider the relationships between argument roles, which somewhat affects the performance of event extraction. To address the nested event challenge in the event extraction domain, this study proposes a cascading type event extraction model based on an argument association graph, called AAGCTEE (Argument Association Graph-based Cascading Type Event Extraction Model). AAGCTEE utilizes XLM-RoBERTa-base for deep encoding and enhances the representation between word pairs through conditional layer normalization and dilated convolution techniques. In the trigger recognition module, AAGCTEE's Cascade Type Predictor (CTP) accurately identifies triggers related to multiple event types, effectively solving the common issue of nested triggers in traditional event extraction. For the argument recognition and classification module, AAGCTEE employs an Argument Association Graph (AAG) and a Global Normalization Decoding (GND) strategy to efficiently handle complex nested argument structures. The average F1 scores for trigger identification, trigger classification, argument identification, and argument classification of AAGCTEE outperform those of comparative models by 9.67%, 9.22%, 18.95%, and 37.31% on Chinese datasets DUEE and FewFC, and English datasets PHEE and CASIE, respectively. Compared with ablation experiments lacking the cascade type predictor, argument association graph, and other components, AAGCTEE demonstrates an average increase of 6.92%, 6.68%, and 6.45% in the average F1 score across the four evaluation metrics, verifying its effectiveness in extracting complex events.