  Ultra-fine entity typing (UFET) is the task of inferring the semantic types,
from a large set of fine-grained candidates, that apply to a given entity
mention. This task is especially challenging because we only have a small
number of training examples for many of the types, even with distant
supervision strategies. State-of-the-art models, therefore, have to rely on
prior knowledge about the type labels in some way. In this paper, we show that
the performance of existing methods can be improved using a simple technique:
we use pre-trained label embeddings to cluster the labels into semantic domains
and then treat these domains as additional types. We show that this strategy
consistently leads to improved results, as long as high-quality label
embeddings are used. We furthermore use the label clusters as part of a simple
post-processing technique, which results in further performance gains. Both
strategies treat the UFET model as a black box and can thus straightforwardly
be used to improve a wide range of existing models.
