基于增强BiLSTM-CRF模型的推文恶意软件名称识别

针对推文中恶意软件名称识别任务存在的文本简短、非正式、实体类别单一以及实体歧义等问题,提出了一种基于BERT-BiLSTM-Self-attention-CRF的实体识别方法,以实现推文中恶意软件名称的自动识别。在BiLSTM-CRF模型的基础上,利用BERT模型编码单词语境信息,提升词嵌入的上下文语义质量,增强原有模型的语义消歧能力;同时,借助Self-attention机制学习单词间关系和句子结构特征,利用加权表征帮助单一类别实体的解码,以提升恶意软件名称实体的识别效果。通过构建包含恶意软件名称实体的推文标记数据集进行实验测试,结果表明,提出的方法可以实现更好的性能,其精确率、召回率、F1值分别为86.38%,84.73%,85.55%,相较于基线模型BiLSTM-CRF,F1值提升了12.61%。

Malware Name Recognition in Tweets Based on Enhanced BiLSTM-CRF Model

To address the problems such as short,informal,single entity category and entity disambiguation in the malware name recognition task on Twitter,this paper proposed an entity recognition method based on BERT-BiLSTM-Self-attention-CRF to automatically recognize malware name in tweets.Based on the BiLSTM-CRF model,the BERT is used to encode context information,improve the contextual semantic quality of word embeddings,and enhance the semantic disambiguation ability.At the same time,Self-attention mechanism is used to learn weighted representation to improve the performance of single entity category re-cognition by learning the long-term relations between words and sentence structure.To evaluate the proposed methods,this paper constructed a labeled dataset in tweets that contains malware name entities.Experimental results show that the proposed method can achieve a better performance,attain 86.38%precision,84.73%recall and 85.55%F-score.The proposed model can outperforms the baseline model,with F-score improved by 12.61%.