Enterprise relation extraction aims to detect pairs of enterprise entities
and identify the business relations between them from unstructured or
semi-structured text data, and it is crucial for several real-world
applications such as risk analysis, rating research and supply chain security.
However, previous work mainly focuses on getting attribute information about
enterprises like personnel and corporate business, and pays little attention to
enterprise relation extraction. To encourage further progress in the research,
we introduce the CEntRE, a new dataset constructed from publicly available
business news data with careful human annotation and intelligent data
processing. Extensive experiments on CEntRE with six excellent models
demonstrate the challenges of our proposed dataset.

该研究旨在从非结构化或半结构化文本数据中检测企业实体对并识别它们之间的业务关系，并介绍了一个名为 CEntRE 的新数据集，它由公开的商业新闻数据构建而成，并通过人工标注和智能数据处理进行了精心处理。基于 CEntRE 数据集的六种模型的广泛实验表明了我们提出的数据集的挑战。