关键词: 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

Abstract: Abstract: To address the limitations of existing complex event extraction methods in event segmentation, particularly their inability to handle cases where the same trigger word evokes multiple events of the same type, a novel framework based on event element relation recognition and complete subgraph search was proposed. First, a concise and innovative word-pair relation labeling schema was designed, incorporating Span relations to identify event element boundaries and EI relations to determine whether elements belonged to the same event. Second, 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 partition event elements, theoretically enabling the exhaustive extraction of all complex event patterns. Experimental results on the Chinese event extraction datasets FewFC and DuEE demonstrated that the proposed method significantly outperformed existing approaches in event-level F1-score, particularly achieving a 21.5% improvement in overlapping events with the same trigger. The framework effectively resolved the issue of same-type trigger overlap and ensured comprehensive extraction of complex events.

Key words: Keywords: Complex Event Extraction, Word-Pair Relation Modeling, Event Partitioning, Maximal Clique Search, Natural Language Processing