Abstract
Event extraction is an important task in the field of natural language processing. However, most of the existing event extraction techniques focus on sentence-level extraction, which inevitably ignores the contextual features of sentences and the occurrence of multiple event trigger words in the same sentence. Therefore, this paper mainly uses the multi-head self-attention mechanism to integrate text features from multiple dimensions and levels to achieve the task of event detection at the level of text. First, convolutional neural network combined with dynamic multi-pool strategy is used to extract sentence level features. Secondly, the discourse feature representation of full-text information fusion is obtained by multi-head self-attention mechanism model. Finally, using the classifier function to classify, and then detect the trigger word and category of the event. Experimental results show that the proposed method achieves good results in document-level event extraction.
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Acknowledgement
The authors would like to thank the associate editor and the reviewers for their time and effort provided to review the manuscript.
Funding
This work is supported by State Grid Shandong Electric Power Company Science and Technology Project Funding under Grant no. 62061320C007, SGSDWH00YXJS2000128, the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF.201714), Weihai Science and Technology Development Program (2016DX GJMS15), Weihai Scientific Research and Innovation Fund (2020) and Key Research and Development Program in Shandong Provincial (2017GGX90103).
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Qiao, X. et al. (2023). A Event Extraction Method of Document-Level Based on the Self-attention Mechanism. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_50
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