  The problem of entity-typing has been studied predominantly in supervised
learning fashion, mostly with task-specific annotations (for coarse types) and
sometimes with distant supervision (for fine types). While such approaches have
strong performance within datasets, they often lack the flexibility to transfer
across text genres and to generalize to new type taxonomies. In this work we
propose a zero-shot entity typing approach that requires no annotated data and
can flexibly identify newly defined types. Given a type taxonomy defined as
Boolean functions of FREEBASE "types", we ground a given mention to a set of
type-compatible Wikipedia entries and then infer the target mention's types
using an inference algorithm that makes use of the types of these entries. We
evaluate our system on a broad range of datasets, including standard
fine-grained and coarse-grained entity typing datasets, and also a dataset in
the biological domain. Our system is shown to be competitive with
state-of-the-art supervised NER systems and outperforms them on out-of-domain
datasets. We also show that our system significantly outperforms other
zero-shot fine typing systems.
