Entity Set Expansion (ESE) is a critical task aiming to expand entities of the target semantic class described by a small seed entity set. Most existing ESE methods are retrieval-based frameworks that need to extract the contextual features of entities and calculate the similarity between seed entities and candidate entities. To achieve the two purposes, they should iteratively traverse the corpus and the entity vocabulary provided in the datasets, resulting in poor efficiency and scalability. The 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 a generative ESE framework, Generative Entity Set Expansion (GenExpan), which utilizes a generative pre-trained 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. Experiments on publicly available datasets show that GenExpan is efficient and effective. 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 performance, our framework outperforms previous state-of-the-art ESE methods.

翻译：实体集合扩展（ESE）是一项关键任务，旨在通过少量种子实体集所描述的目标语义类别来扩展实体。现有的多数ESE方法均基于检索框架，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。为实现这两个目标，它们需迭代遍历语料库及数据集中提供的实体词汇表，导致效率与可扩展性较差。实验结果表明，基于检索的ESE方法所消耗的时间随实体词汇表及语料库规模线性增长。本文首次提出一种生成式ESE框架——生成式实体集合扩展（GenExpan），该框架利用生成式预训练语言模型完成ESE任务。具体而言，采用前缀树确保实体生成的有效性，并利用自动生成的类别名称引导模型生成目标实体。此外，我们提出知识校准与生成排序方法，以进一步弥合语言模型的通用知识与ESE任务目标之间的差距。在公开数据集上的实验表明，GenExpan兼具高效性与有效性。在效率方面，GenExpan的扩展时间消耗与实体词汇表及语料库规模无关，相比强基线方法平均实现600%的加速。在扩展性能方面，我们的框架优于以往最先进的ESE方法。