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We propose a graph-based event extraction framework JSEEGraph that approachesthe task of event extraction as general graph parsing in the tradition ofMeaning Representation Parsing. It explicitly encodes entities and events in asingle semantic graph, and further has the flexibility to encode a wider rangeof additional IE relations and jointly infer individual tasks. JSEEGraphperforms in an end-to-end manner via general graph parsing: (1) instead of flatsequence labelling, nested structures between entities/triggers are efficientlyencoded as separate nodes in the graph, allowing for nested and overlappingentities and triggers; (2) both entities, relations, and events can be encodedin the same graph, where entities and event triggers are represented as nodesand entity relations and event arguments are constructed via edges; (3) jointinference avoids error propagation and enhances the interpolation of differentIE tasks. We experiment on two benchmark datasets of varying structuralcomplexities; ACE05 and Rich ERE, covering three languages: English, Chinese,and Spanish. Experimental results show that JSEEGraph can handle nested eventstructures, that it is beneficial to solve different IE tasks jointly, and thatevent argument extraction in particular benefits from entity extraction. Ourcode and models are released as open-source.

JSEEGraph: Joint Structured Event Extraction as Graph Parsing