Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. Manually predefined event schemas have limited coverage and are hard to migrate across domains. Therefore, the researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously. However, existing LEE models rely heavily on external language knowledge bases and require the manual development of numerous rules for noise removal and knowledge alignment, which is complex and laborious. To this end, we propose a Prompt-based Graph Model for Liberal Event Extraction (PGLEE). Specifically, we use a prompt-based model to obtain candidate triggers and arguments, and then build heterogeneous event graphs to encode the structures within and between events. Experimental results prove that our approach achieves excellent performance with or without predefined event schemas, while the automatically detected event schemas are proven high quality.

翻译：事件是言语和文本的基本组成部分，描述实体的状态变化。事件抽取任务旨在根据事件模式识别并分类事件，同时找出其参与者。人工预定义的事件模式覆盖范围有限且难以跨领域迁移，因此研究者提出自由事件抽取（Liberal Event Extraction, LEE），旨在同时抽取事件并发现事件模式。然而，现有LEE模型严重依赖外部语言知识库，需要手动制定大量规则进行噪声去除和知识对齐，流程复杂且费力。为此，我们提出基于提示的图模型用于自由事件抽取（Prompt-based Graph Model for Liberal Event Extraction, PGLEE）。具体而言，我们利用基于提示的模型获取候选触发词和论元，进而构建异构事件图以编码事件内部及事件间的结构关系。实验结果证明，无论是否使用预定义事件模式，我们的方法均能取得优异性能，且自动检测到的事件模式被证实具有高质量。