  Conventional closed-world information extraction (IE) approaches rely on
human ontologies to define the scope for extraction. As a result, such
approaches fall short when applied to new domains. This calls for systems that
can automatically infer new types from given corpora, a task which we refer to
as type discovery. To tackle this problem, we introduce the idea of type
abstraction, where the model is prompted to generalize and name the type. Then
we use the similarity between inferred names to induce clusters. Observing that
this abstraction-based representation is often complementary to the
entity/trigger token representation, we set up these two representations as two
views and design our model as a co-training framework. Our experiments on
multiple relation extraction and event extraction datasets consistently show
the advantage of our type abstraction approach. Code available at
https://github.com/raspberryice/type-discovery-abs.
