A dataset for document level Chinese financial event extraction

Abstract

Financial event modeling is fundamental to financial investment decisions and risk management, crucial for the stability and growth of financial institutions, and helps ensure the stability and quality of people’s lives. Utilizing state-of-the-art natural language processing techniques for automated financial event extraction addresses the inefficiencies and high costs associated with traditional event identification and modeling, which rely heavily on financial domain experts. However, existing datasets fail to tackle the issues with long documents in practical situations. To address this, we first propose DocFEE, a large-scale Document-level Chinese Financial Event Extraction dataset. It reflects the length of announcement documents and the long-distance dependencies of event arguments in real-world scenarios.