Research on event trigger word extraction based on semantic enhancement

Qimin Yang; Yonggang Zhang; Xuhong Liu; Zhanqi Cui; Lin Qi; Xiulei Liu

doi:10.1117/12.2674375

28 April 2023 Research on event trigger word extraction based on semantic enhancement

Qimin Yang, Yonggang Zhang, Xuhong Liu, Zhanqi Cui, Lin Qi, Xiulei Liu

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260B (2023) https://doi.org/10.1117/12.2674375
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

With the rapid development of Internet information technology, more and more information pours into the Internet. Information extraction technology came into being, in which event extraction technology is the focus and difficulty. Event trigger word extraction is the first important stage of event extraction. Aiming at the problem of multiple triggers in an event mention, an event trigger extraction model based on semantic enhancement is proposed. In the pre-processing stage, by optimizing the MLM mask mechanism and integrating external knowledge such as element embedding information and Chinese military event terminology, the model can obtain more reliable Chinese military language representation. Then BiLSTM is used to mine the context features of Chinese military news long texts. Finally, CRF is used to assist label correction to achieve event trigger word extraction. Based on the data set constructed in this paper, the effectiveness of the event trigger word extraction model is verified. This method will provide some reference for domain event trigger word extraction.

Citation Download Citation

Qimin Yang, Yonggang Zhang, Xuhong Liu, Zhanqi Cui, Lin Qi, and Xiulei Liu "Research on event trigger word extraction based on semantic enhancement", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260B (28 April 2023); https://doi.org/10.1117/12.2674375

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KEYWORDS

Semantics

Transformers

Machine learning

Mathematical optimization

Education and training

Engineering

Data conversion

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