Improved self-training-based distant label denoising method for cybersecurity entity extractions.

Autor: Zhang, Ke, Wang, Yunpeng, Li, Ou, Hao, Sirui, He, Junjiang, Lan, Xiaolong, Yang, Jinneng, Ye, Yang
Zdroj: PLoS ONE; 12/17/2024, Vol. 19 Issue 12, p1-16, 16p
Abstrakt: The task of named entity recognition (NER) plays a crucial role in extracting cybersecurity-related information. Existing approaches for cybersecurity entity extraction predominantly rely on manual labelling data, resulting in labour-intensive processes due to the lack of a cybersecurity-specific corpus. In this paper, we propose an improved self-training-based distant label denoising method for cybersecurity entity extraction. Firstly, we create two domain dictionaries of cybersecurity. Then, an algorithm that combines reverse maximum matching and part-of-speech tagging restrictions is proposed, for generating distant labels for the cybersecurity domain corpus. Lastly, we propose a high-confidence text selection method and an improved self-training algorithm that incorporates a teacher-student model and weight update constraints, for exploring the true labels of low-confidence text using a model trained on high-confidence text, thereby reducing the noise in the distant annotation data. Experimental results demonstrate that the cybersecurity distantly-labelled data we obtained is of high quality. Additionally, the proposed constrained self-training algorithm effectively improves the F1 score of several state-of-the-art NER models on this dataset, yielding a 3.5% improvement for the Vendor class and a 3.35% improvement for the Product class. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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