Prompt-based Graph Model for Joint Liberal Event Extraction and Event
  Schema Induction
Haochen Li Di Geng
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.