Joint contrastive learning and belief rule base for named entity recognition in cybersecurity

Joint contrastive learning and belief rule base for named entity recognition in cybersecurity

Abstract Named Entity Recognition (NER) in cybersecurity is crucial for mining information during cybersecurity incidents. Current methods rely on pre-trained models for rich semantic text embeddings, but the challenge of anisotropy may affect subsequent encoding quality. Additionally, existing mode...

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Bibliographic Details
Main Authors: Chenxi Hu, Tao Wu, Chunsheng Liu, Chao Chang
Format: Article
Language:English
Published: SpringerOpen 2024-04-01
Series:Cybersecurity
Subjects:
Named entity recognition
Cybersecurity
Contrastive learning
Belief rule base
Online Access:https://doi.org/10.1186/s42400-024-00206-y

Internet

https://doi.org/10.1186/s42400-024-00206-y

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