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Research on the Financial Event Extraction Method Based on Fin-BERT Cover
By: Jing He and  Yongyong Sun  
Open Access
|Dec 2024

Figures & Tables

Figure 1.

Model framework
Model framework

Figure 2.

The trained model of Fin-Bert
The trained model of Fin-Bert

Figure 3.

RAAT structure
RAAT structure

Figure 4.

Types of Events Extracted Resulting F1 Values
Types of Events Extracted Resulting F1 Values

experimental extraxtion of chfinann datasets

PRF1
Doc2EDAG80.370.577.5
GIT82.378.480.3
PTPCG88.269.179.4
Fin-RAAT84.079.981.9

comparison of event extraction experiment results in chfinann datasets

F1
SingleMultiAll
Doc2EDAG81.067.477.5
GIT87.672.380.3
PTPCG88.269179.4
Fin-RAAT87.975.381.9

datasets information

Training dataValidation dataTest dataEvent types
ChFinAnn25632320432045
Duee-fin70151171About 350013

duee-fin datasets

DevOnline test
PRF1PRF1
Doc2EDAG73.759.866.067.151.358.1
GIT75.461.467.770.346.055.6
PTPCG71.061.766.066.754.660.0
Fin-RAAT76.171.373.070.356.162.8

experimental enviroment

Hardware InformationConfigure
Operating systemWindows 10
Internal storage32GB
CPUAMD R9-5900HX
Video cardRTX-3080-LAPTOP
Memory16GB
Exploitation environmentPython 3.7
Language: English
Page range: 67 - 74
Published on: Dec 31, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Jing He, Yongyong Sun, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.