  Existing event-centric NLP models often only apply to the pre-defined
ontology, which significantly restricts their generalization capabilities. This
paper presents CEO, a novel Corpus-based Event Ontology induction model to
relax the restriction imposed by pre-defined event ontologies. Without direct
supervision, CEO leverages distant supervision from available summary datasets
to detect corpus-wise salient events and exploits external event knowledge to
force events within a short distance to have close embeddings. Experiments on
three popular event datasets show that the schema induced by CEO has better
coverage and higher accuracy than previous methods. Moreover, CEO is the first
event ontology induction model that can induce a hierarchical event ontology
with meaningful names on eleven open-domain corpora, making the induced schema
more trustworthy and easier to be further curated.
