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Real-Time Extraction of News Events Based on BERT Model Cover
By: Yuxin Jiao and  Li Zhao  
Open Access
|Sep 2024

Figures & Tables

Figure 1.

Event Extraction Process Map

Figure 2.

Pre-training and Fine-Tune process

Figure 3.

Graph structure of CRFs for linear chain conditional random fields

Figure 4.

Extracted event output structure, including event types and argument roles

Figure 5.

Comparison of P-value, R-value and F1-value of LSTM, BiLSTM and BERT-CRF models. P for Precision, R for Recall

Figure 6.

Comparison of P-value, R-value and F1-value of BERT, RoBERTa and ALBERT models Comparison of P, R and F1 values. P for Precision, R for Recall

Experimental Results I

ModulePRF1
LSTM34.2%40.6%37.1%
BiLSTM58.1%56.2%57.1%
BERT-CRF76.5%76.9%76.7%

Experimental Results II

ModulePRF1
BERT76.50%76.90%76.70%
RoBERTa77.60%77.10%77.30%
ALBERT78.10%77.30%77.70%
Language: English
Page range: 24 - 31
Published on: Sep 30, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Yuxin Jiao, Li Zhao, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.