Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various NLP and IR downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing bootstrapping methods have achieved great progress, most of them still rely on manually pre-defined context patterns. A non-negligible shortcoming of the pre-defined context patterns is that they cannot be flexibly generalized to all kinds of semantic classes, and we call this phenomenon as "semantic sensitivity". To address this problem, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments will be available for reproducibility.

本研究提出了一种新的实体集扩展(ESE)范例，即独立于语料库的ESE，并使用自回归语言模型自动生成高质量的上下文模式，通过GAPA框架扩展目标实体，并在三个广泛使用的数据集上进行了全面的实验和详细的分析，结果表明该方法非常有效。

实体集扩展的自动生成上下文模式