基于BERT的多层标签指针网络事件抽取模型——2020语言与智能技术竞赛事件抽取任务系统报告

事件抽取(event extraction, EE)是指从自然语言文本中抽取事件并识别事件类型和事件元素的技术,是智能风控、智能投研、舆情监测等人工智能应用的重要技术基础。该文提出一种端到端的多标签指针网络事件抽取方法,并将事件检测任务融入到事件元素识别任务中,达到同时抽取事件元素及事件类型的目的。该方法避免了传统管道式方法存在的错误级联和任务割裂问题,同时也解决了事件抽取中存在的角色重叠和元素重叠问题。该文提出的事件抽取方法在2020语言与智能技术竞赛——事件抽取任务测试集上中取得85.9%的F₁值。

BERT Based Multi-layer Label Pointer Network for Event Extraction

Event extraction (EE) refers to the technology of extracting events from natural language texts and identifying event types and event elements. This paper proposes an end-to-end multi-label pointer network for event extraction, in which the event detection task is integrated into the event element recognition task to extract event elements and event types at the same time. This method avoids the problem of wrong cascade and task separation in traditional pipeline methods, and alleviates the problem of role overlapping and element overlapping in event extraction. The proposed method achieves 85.9% F₁ score on the test set in 2020 Language and Intelligence Challenge Event Extraction task.