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<p>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 <b>Doc</b>ument-level Chinese <b>F</b>inancial <b>E</b>vent <b>E</b>xtraction dataset. It reflects the length of announcement documents and the long-distance dependencies of event arguments in real-world scenarios.</p>

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A dataset for document level Chinese financial event extraction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月10日

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  • Yubo Chen,
  • Tong Zhou,
  • Sirui Li,
  • Jun Zhao

摘要

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.