JSEEGraph: Joint Structured Event Extraction as Graph Parsing
Huiling You Samia Touileb Lilja Øvrelid
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.