CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises

TL;DR

This paper introduces CEntRE, a new paragraph-level Chinese dataset for enterprise relation extraction, highlighting its construction, challenges, and potential for advancing research in business relation understanding.

Contribution

The paper presents a novel, carefully annotated Chinese dataset for enterprise relation extraction, addressing a gap in existing attribute-focused datasets.

Findings

Six models tested show the dataset's complexity and challenges.

The dataset enables better understanding of enterprise relations from news data.

Highlights the need for improved models for relation extraction.

Abstract

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.