Abstract:

In response to the limitations of the existing complex Event Extraction （EE） methods in event classification， particularly their inability to handle the issue of a single trigger word activating multiple events of the same type， a complex EE method based on event element relation recognition and complete subgraph search was proposed to improve the effects of complex event classification. Firstly， a concise word-pair relation labeling system was designed， incorporating Span relations to identify event element boundaries and Event-Internal （EI） relations to determine whether elements belonged to the same event. Secondly， a single-stage word-pair relation recognition model was constructed， where text representations were obtained through an encoding layer， event type information was injected via an event information fusion layer， and word-pair relations were predicted using a distance-aware scoring function in the prediction layer. Finally， based on the predicted EI relations， an undirected graph was built， and a recursive complete subgraph search algorithm was designed to classify event elements， thereby enabling the complete extraction complex event of all patterns theoretically. Experimental results show that the proposed method outperforms various baselines like BERT-CRF-joint， PLMEE （Pre-trained Language Model for EE）， and CasEE （Cascade decoding for EE） in complex EE on the FewFC （Few-shot Financial Corpus） and DuEE （Dataset for Chinese EE） datasets. It can be seen that the method addresses the issue of a single trigger word activating multiple events of the same type effectively， leading to a comprehensive extraction of complex events.

Key words: complex Event Extraction (EE), word-pair relation modeling, event classification, complete subgraph search, Natural Language Processing (NLP)

摘要：

针对现有复杂事件抽取（EE）方法在事件划分上存在的缺陷，尤其是无法处理同一触发词触发多个相同类型事件的问题，提出一种基于事件要素关系识别和完全子图搜索的复杂EE方法改进复杂事件划分的效果。首先，设计一种简洁的词对关系标签体系，包含Span关系用于识别事件要素的边界，以及事件内部（EI）关系用于表示事件要素是否属于同一事件；其次，构建单阶段词对关系识别模型，通过编码层获取文本表示，使用事件信息融合层注入事件类型信息，并在预测层使用距离感知的打分函数识别词对关系；最后，基于预测的EI关系构建无向图，设计一个递归的完全子图搜索算法划分事件要素，理论上完备地抽取所有模式的复杂事件。实验结果表明，所提方法在FewFC（Few-shot Financial Corpus）和DuEE（Dataset for Chinese EE）数据集上的复杂EE表现优于BERT-CRF-joint、PLMEE（Pre-trained Language Model for EE）和CasEE（Cascade decoding for EE）等多种基线模型，有效解决了同类型触发词触发多个相同类型事件的问题，能够较全面地抽取复杂事件。

关键词: 复杂事件抽取, 词对关系建模, 事件划分, 完全子图搜索, 自然语言处理