As network technology rapidly advanced,new cybersecurity threats constantly emerged,increasing the impor-tance of cybersecurity named entity recognition.To address the problem of poor recognition accuracy in named entity recognition methods based on large language models in the cybersecurity domain,a novel cybersecurity named entity recognition method that combined soft prompt tuning and reinforcement learning was proposed.By integrating the soft prompt tuning technique,the method precisely adjusted the recognition capabilities of large language models to handle the complexity of the cybersecurity domain,improving recognition accuracy for cybersecurity named entities while opti-mizing training efficiency.Additionally,a reinforcement learning-based instance filter was proposed,which effectively removed low-quality annotations from the training set,further enhancing recognition accuracy.The proposed method was evaluated on two benchmark cybersecurity NER datasets,with experimental results demonstrating superior perfor-mance in F1 score compared to state-of-the-art cybersecurity NER methods.
关键词
网络安全命名实体识别/软提示微调/强化学习/大规模预训练模型
Key words
cybersecurity named entity recognition/soft prompt tuning/reinforcement learning/large-scale pre-trained models
维普
万方数据