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Entity set expansion, aiming at expanding a small seed entity set with newentities belonging to the same semantic class, is a critical task that benefitsmany downstream NLP and IR applications, such as question answering, queryunderstanding, and taxonomy construction. Existing set expansion methodsbootstrap the seed entity set by adaptively selecting context features andextracting new entities. A key challenge for entity set expansion is to avoidselecting ambiguous context features which will shift the class semantics andlead to accumulative errors in later iterations. In this study, we propose anovel iterative set expansion framework that leverages automatically generatedclass names to address the semantic drift issue. In each iteration, we selectone positive and several negative class names by probing a pre-trained languagemodel, and further score each candidate entity based on selected class names.Experiments on two datasets show that our framework generates high-qualityclass names and outperforms previous state-of-the-art methods significantly.

Empower Entity Set Expansion via Language Model Probing