  Set expansion aims to expand a small set of seed entities into a complete set
of relevant entities. Most existing approaches assume the input seed set is
unambiguous and completely ignore the multi-faceted semantics of seed entities.
As a result, given the seed set {"Canon", "Sony", "Nikon"}, previous models
return one mixed set of entities that are either Camera Brands or Japanese
Companies. In this paper, we study the task of multi-faceted set expansion,
which aims to capture all semantic facets in the seed set and return multiple
sets of entities, one for each semantic facet. We propose an unsupervised
framework, FUSE, which consists of three major components: (1) facet discovery
module: identifies all semantic facets of each seed entity by extracting and
clustering its skip-grams, and (2) facet fusion module: discovers shared
semantic facets of the entire seed set by an optimization formulation, and (3)
entity expansion module: expands each semantic facet by utilizing a masked
language model with pre-trained BERT models. Extensive experiments demonstrate
that FUSE can accurately identify multiple semantic facets of the seed set and
generate quality entities for each facet.
