FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams
This addresses the limitation in set expansion for ambiguous seed sets, enabling more precise entity retrieval in domains like information extraction.
The paper tackles the problem of multi-faceted set expansion, where seed entities have multiple meanings, and proposes FUSE, an unsupervised framework that identifies semantic facets and expands entities for each, achieving accurate results in experiments.
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.