  Entity typing is the task of assigning semantic types to the entities that
are mentioned in a text. In the case of fine-grained entity typing (FET), a
large set of candidate type labels is considered. Since obtaining sufficient
amounts of manual annotations is then prohibitively expensive, FET models are
typically trained using distant supervision. In this paper, we propose to
improve on this process by pre-training an entity encoder such that embeddings
of coreferring entities are more similar to each other than to the embeddings
of other entities. The main problem with this strategy, which helps to explain
why it has not previously been considered, is that predicted coreference links
are often too noisy. We show that this problem can be addressed by using a
simple trick: we only consider coreference links that are predicted by two
different off-the-shelf systems. With this prudent use of coreference links,
our pre-training strategy allows us to improve the state-of-the-art in
benchmarks on fine-grained entity typing, as well as traditional entity
extraction.
