  Entity Set Expansion is an important NLP task that aims at expanding a small
set of entities into a larger one with items from a large pool of candidates.
In this paper, we propose GausSetExpander, an unsupervised approach based on
optimal transport techniques. We propose to re-frame the problem as choosing
the entity that best completes the seed set. For this, we interpret a set as an
elliptical distribution with a centroid which represents the mean and a spread
that is represented by the scale parameter. The best entity is the one that
increases the spread of the set the least. We demonstrate the validity of our
approach by comparing to state-of-the art approaches.
