TFOEE: an event extraction model for police text

Zirong Su; Yongbing Gao; Xiaoang Chen; Lidong Yang

doi:10.1117/12.3057733

3 January 2025 TFOEE: an event extraction model for police text

Zirong Su, Yongbing Gao, Xiaoang Chen, Lidong Yang

Author Affiliations +

Proceedings Volume 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024); 135190H (2025) https://doi.org/10.1117/12.3057733
Event: Third International Conference on Communications, Information System and Data Science 2024, 2024, Nanjing, China

Abstract

In the event extraction task, the existing models use trigger words as a bridge to extract structured information, but the extraction effect is not ideal when faced with police texts without trigger words or fixed trigger words. To solve this problem, an end-to-end trigger-free word overlapping event extraction model was proposed—TFOEE. In this model, the task of extracting overlapping events without triggering words is transformed into a task of identifying relationships based on grid filling strategy, event types and word fragments. Experiments show that the accuracy, recall rate and F1 value of TFOEE model are better than those of baseline model on police text dataset. And the F1 value of the TFOEE model reached 94.1%.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zirong Su, Yongbing Gao, Xiaoang Chen, and Lidong Yang "TFOEE: an event extraction model for police text", Proc. SPIE 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024), 135190H (3 January 2025); https://doi.org/10.1117/12.3057733

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