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Set expansion aims to expand a small set of seed entities into a complete setof relevant entities. Most existing approaches assume the input seed set isunambiguous and completely ignore the multi-faceted semantics of seed entities.As a result, given the seed set {"Canon", "Sony", "Nikon"}, previous modelsreturn one mixed set of entities that are either Camera Brands or JapaneseCompanies. 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 multiplesets of entities, one for each semantic facet. We propose an unsupervisedframework, FUSE, which consists of three major components: (1) facet discoverymodule: identifies all semantic facets of each seed entity by extracting andclustering its skip-grams, and (2) facet fusion module: discovers sharedsemantic facets of the entire seed set by an optimization formulation, and (3)entity expansion module: expands each semantic facet by utilizing a maskedlanguage model with pre-trained BERT models. Extensive experiments demonstratethat FUSE can accurately identify multiple semantic facets of the seed set andgenerate quality entities for each facet.

FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams