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.

翻译：实体集扩展（ESE）是一项关键任务，旨在扩充由种子实体描述的目标语义类别的实体。现有的大部分ESE方法采用基于检索的框架，需要提取实体的上下文特征并计算种子实体与候选实体之间的相似度。为了实现这两个目的，它们需迭代遍历语料库和实体词典，导致效率和可扩展性较差。实验结果表明，基于检索的ESE方法所消耗的时间随实体词典和语料库规模线性增长。本文首次提出生成式实体集扩展框架GenExpan，利用预训练的生成式自回归语言模型完成ESE任务。具体而言，采用前缀树保证实体生成的有效性，并利用自动生成的类名引导模型生成目标实体。此外，我们提出知识校准与生成式排序，以进一步弥合语言模型通用知识与ESE任务目标之间的差距。在效率方面，GenExpan的扩展时间与实体词典和语料库规模无关，相比强基线方法平均加速600%。在扩展效果上，我们的框架超越此前最优的ESE方法。