  Most event extraction methods have traditionally relied on an annotated set
of event types. However, creating event ontologies and annotating supervised
training data are expensive and time-consuming. Previous work has proposed
semi-supervised approaches which leverage seen (annotated) types to learn how
to automatically discover new event types. State-of-the-art methods, both
semi-supervised or fully unsupervised, use a form of reconstruction loss on
specific tokens in a context. In contrast, we present a novel approach to
semi-supervised new event type induction using a masked contrastive loss, which
learns similarities between event mentions by enforcing an attention mechanism
over the data minibatch. We further disentangle the discovered clusters by
approximating the underlying manifolds in the data, which allows us to increase
normalized mutual information and Fowlkes-Mallows scores by over 20% absolute.
Building on these clustering results, we extend our approach to two new tasks:
predicting the type name of the discovered clusters and linking them to
FrameNet frames.
