Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) and Information Retrieval (IR) downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing corpus-based ESE methods have achieved great progress, they still rely on corpora with high-quality entity information annotated, because most of them need to obtain the context patterns through the position of the entity in a sentence. Therefore, the quality of the given corpora and their entity annotation has become the bottleneck that limits the performance of such methods. To overcome this dilemma and make the ESE models free from the dependence on entity annotation, our work aims to explore a new ESE paradigm, namely corpus-independent ESE. Specifically, 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 are available at https://github.com/geekjuruo/GAPA.

翻译：实体集扩展（ESE）是一项有价值的任务，旨在根据给定种子实体描述的目标语义类别发现实体。由于ESE具备知识发现能力，多种自然语言处理（NLP）和信息检索（IR）下游应用已从中受益。尽管现有的基于语料库的ESE方法取得了重大进展，但它们仍依赖于标注了高质量实体信息的语料库，因为大多数方法需要根据句子中实体的位置获取上下文模式。因此，给定语料库及其实体标注的质量已成为限制此类方法性能的瓶颈。为突破这一困境、使ESE模型摆脱对实体标注的依赖，本文致力于探索一种新的ESE范式——语料无关ESE。具体而言，我们设计了一个上下文模式生成模块，利用自回归语言模型（如GPT-2）自动为实体生成高质量的上下文模式。此外，我们提出了GAPA这一新型ESE框架，利用上述生成模式来扩展目标实体。在三个广泛使用的数据集上的大量实验和详细分析证明了我们方法的有效性。我们实验的所有代码均可从 https://github.com/geekjuruo/GAPA 获取。