  Entity set expansion, aiming at expanding a small seed entity set with new
entities belonging to the same semantic class, is a critical task that benefits
many downstream NLP and IR applications, such as question answering, query
understanding, and taxonomy construction. Existing set expansion methods
bootstrap the seed entity set by adaptively selecting context features and
extracting new entities. A key challenge for entity set expansion is to avoid
selecting ambiguous context features which will shift the class semantics and
lead to accumulative errors in later iterations. In this study, we propose a
novel iterative set expansion framework that leverages automatically generated
class names to address the semantic drift issue. In each iteration, we select
one positive and several negative class names by probing a pre-trained language
model, and further score each candidate entity based on selected class names.
Experiments on two datasets show that our framework generates high-quality
class names and outperforms previous state-of-the-art methods significantly.
