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...
| 出版年: | Cybersecurity |
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| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
SpringerOpen
2024-04-01
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1186/s42400-024-00206-y |