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A dataset for document level Chinese financial event extraction.

Yubo Chen1,2, Tong Zhou3, Sirui Li4

  • 1The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. yubo.chen@nlpr.ia.ac.cn.

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Summary
This summary is machine-generated.

This study introduces DocFEE, a large dataset for financial event extraction from long Chinese documents. It addresses limitations in current datasets for real-world financial event modeling.

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Area of Science:

  • Natural Language Processing
  • Financial Technology
  • Data Science

Background:

  • Financial event modeling is crucial for investment decisions and risk management.
  • Traditional methods rely on costly expert analysis.
  • Existing datasets lack suitability for long documents common in finance.

Purpose of the Study:

  • To address the limitations of current datasets in financial event extraction.
  • To propose a large-scale dataset for document-level Chinese financial event extraction.
  • To facilitate more accurate and efficient financial event modeling.

Main Methods:

  • Development of DocFEE, a novel dataset for document-level financial event extraction.
  • Focus on Chinese financial announcement documents.
  • Capturing long-distance dependencies between event arguments.

Main Results:

  • DocFEE is a large-scale dataset reflecting real-world document lengths.
  • The dataset captures long-distance argument dependencies.
  • Enables improved automated financial event extraction from lengthy texts.

Conclusions:

  • DocFEE enhances the capability of natural language processing in financial event extraction.
  • The dataset supports more robust financial event modeling for investment and risk management.
  • Addresses a critical gap in existing resources for Chinese financial text analysis.