Version 1
: Received: 13 September 2024 / Approved: 14 September 2024 / Online: 14 September 2024 (09:44:37 CEST)
How to cite:
Duan, J.; Zhang, X. Chinese Event Trigger Recommendation Model for High-Accuracy Applications. Preprints2024, 2024091129. https://doi.org/10.20944/preprints202409.1129.v1
Duan, J.; Zhang, X. Chinese Event Trigger Recommendation Model for High-Accuracy Applications. Preprints 2024, 2024091129. https://doi.org/10.20944/preprints202409.1129.v1
Duan, J.; Zhang, X. Chinese Event Trigger Recommendation Model for High-Accuracy Applications. Preprints2024, 2024091129. https://doi.org/10.20944/preprints202409.1129.v1
APA Style
Duan, J., & Zhang, X. (2024). Chinese Event Trigger Recommendation Model for High-Accuracy Applications. Preprints. https://doi.org/10.20944/preprints202409.1129.v1
Chicago/Turabian Style
Duan, J. and Xin Zhang. 2024 "Chinese Event Trigger Recommendation Model for High-Accuracy Applications" Preprints. https://doi.org/10.20944/preprints202409.1129.v1
Abstract
Event detection provides a crucial foundation for intelligent text analysis and supports numerous downstream applications. However, existing detection models often fail to meet the high accuracy requirements and typically require a human–machine collaborative detection mode involving “machine detection followed by manual audit and correction.” Analysis reveals that the recall rate of machine detection is a major factor limiting the efficiency of human–machine collaboration. To address this at its root and ensure high recall, an event trigger recommendation task is introduced, and a span-based recommendation model for Chinese, SpETR (Span-based Event Trigger Recommender), is developed. This model integrates confidence information from both trigger identification and event classification stages to recommend candidate triggers and their event types for each event. To enhance recall, a semantic boundary smoothing method is proposed, which assigns soft labels to spans around gold triggers based on semantic completeness and part-of-speech, combined with a modified focal loss function to make the model predictions smoother. Experiments on DuEE and a self-built dataset show that SpETR is most effective when the number of recommendations is set to 3, achieving recall rates of 97.76% and 98.70%, respectively, comprising improvements of 4.30% and 5.22% over the optimal baseline models. In terms of human–machine collaborative detection, SpETR saved 30.6% and 25.8% of time compared to traditional detection models on two datasets, and the average F1 score of the annotation results was also improved by 5.35% and 3.48%, respectively.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
