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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
Event Extraction Process Map

Figure 2.

Pre-training and Fine-Tune process
Pre-training and Fine-Tune process

Figure 3.

Graph structure of CRFs for linear chain conditional random fields
Graph structure of CRFs for linear chain conditional random fields

Figure 4.

Extracted event output structure, including event types and argument roles
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
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
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.