  Traditional event extraction methods require predefined event types and their
corresponding annotations to learn event extractors. These prerequisites are
often hard to be satisfied in real-world applications. This work presents a
corpus-based open-domain event type induction method that automatically
discovers a set of event types from a given corpus. As events of the same type
could be expressed in multiple ways, we propose to represent each event type as
a cluster of <predicate sense, object head> pairs. Specifically, our method (1)
selects salient predicates and object heads, (2) disambiguates predicate senses
using only a verb sense dictionary, and (3) obtains event types by jointly
embedding and clustering <predicate sense, object head> pairs in a latent
spherical space. Our experiments, on three datasets from different domains,
show our method can discover salient and high-quality event types, according to
both automatic and human evaluations.
