Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This article introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token–event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.

文档级事件提取是一个长期存在的具有挑战性的信息检索问题，涉及一系列子任务：实体提取、事件类型判断和特定于事件类型的多事件提取。然而，将问题作为多个学习任务来解决会导致模型复杂性增加。此外，现有方法没有充分利用跨不同事件的实体的相关性，导致事件提取性能有限。本文介绍了一种用于文档级事件提取的新颖框架，结合了称为令牌事件角色的新数据结构和多通道参数角色预测模块。所提出的数据结构使我们的模型能够揭示令牌在多个事件中的主要作用，从而有助于更全面地理解事件关系。通过利用多通道预测模块，我们将实体和多事件提取转化为预测令牌事件对的单个任务，从而减少整体参数大小并提高模型效率。结果表明，我们的方法在F 1 分数方面比最先进的方法高出 9.5 个百分点，凸显了其在事件提取方面的优越性能。此外，消融研究证实了所提出的数据结构在改进事件提取任务方面的重要价值，进一步验证了其在提高框架整体性能方面的重要性。