We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing. It explicitly encodes entities and events in a single semantic graph, and further has the flexibility to encode a wider range of additional IE relations and jointly infer individual tasks. JSEEGraph performs in an end-to-end manner via general graph parsing: (1) instead of flat sequence labelling, nested structures between entities/triggers are efficiently encoded as separate nodes in the graph, allowing for nested and overlapping entities and triggers; (2) both entities, relations, and events can be encoded in the same graph, where entities and event triggers are represented as nodes and entity relations and event arguments are constructed via edges; (3) joint inference avoids error propagation and enhances the interpolation of different IE tasks. We experiment on two benchmark datasets of varying structural complexities; ACE05 and Rich ERE, covering three languages: English, Chinese, and Spanish. Experimental results show that JSEEGraph can handle nested event structures, that it is beneficial to solve different IE tasks jointly, and that event argument extraction in particular benefits from entity extraction. Our code and models are released as open-source.

翻译：我们提出了一种基于图的事件抽取框架JSEEGraph，该框架将事件抽取任务视为类似于意义表示解析传统中的通用图解析。它在一个语义图中显式编码实体和事件，并进一步具备编码更广泛的信息抽取关系以及联合推理各子任务的灵活性。JSEEGraph通过通用图解析以端到端方式运行：（1）与扁平序列标注不同，实体/触发词之间的嵌套结构被高效地编码为图中的独立节点，从而支持嵌套和重叠的实体与触发词；（2）实体、关系和事件均可编码在同一图中，其中实体和事件触发词表示为节点，实体关系和事件论元通过边构建；（3）联合推理避免了错误传播并增强了不同信息抽取任务之间的相互插值。我们在两个结构复杂度不同的基准数据集（ACE05和Rich ERE）上进行了实验，涵盖英语、中文和西班牙语三种语言。实验结果表明，JSEEGraph能够处理嵌套事件结构，联合求解不同信息抽取任务具有优势，且事件论元抽取尤其受益于实体抽取。我们的代码和模型已作为开源资源发布。