  Entity Set Expansion (ESE) is a critical task aiming at expanding entities of
the target semantic class described by seed entities. Most existing ESE methods
are retrieval-based frameworks that need to extract contextual features of
entities and calculate the similarity between seed entities and candidate
entities. To achieve the two purposes, they iteratively traverse the corpus and
the entity vocabulary, resulting in poor efficiency and scalability.
Experimental results indicate that the time consumed by the retrieval-based ESE
methods increases linearly with entity vocabulary and corpus size. In this
paper, we firstly propose Generative Entity Set Expansion (GenExpan) framework,
which utilizes a generative pre-trained auto-regressive language model to
accomplish ESE task. Specifically, a prefix tree is employed to guarantee the
validity of entity generation, and automatically generated class names are
adopted to guide the model to generate target entities. Moreover, we propose
Knowledge Calibration and Generative Ranking to further bridge the gap between
generic knowledge of the language model and the goal of ESE task. For
efficiency, expansion time consumed by GenExpan is independent of entity
vocabulary and corpus size, and GenExpan achieves an average 600% speedup
compared to strong baselines. For expansion effectiveness, our framework
outperforms previous state-of-the-art ESE methods.
