Empower Entity Set Expansion via Language Model Probing

Understand

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.