基于深度主动学习的信息安全领域命名实体识别研究

Cyber security named entity recognition based on deep active learning

针对通用领域模型不能很好地解决信息安全领域的命名实体识别问题,提出一种基于字符特性,双向长短时记忆网络(Bi-LSTM)与条件随机场(CRF)相结合的信息安全领域命名实体识别方法.该方法不依赖于人工选取特征,通过神经网络模型对序列进行标注,再利用CRF对序列标签的相关性进行约束,提高序列标注的准确性.而且,针对信息安全领域标注数据样本不足的问题,采用主动学习方法,使用少量标注样本达到较好的序列标注效果.

To solve the problem of low accuracy in general cyber security named entity recognition(NER) model,a deep active learning method is proposed for NER in general cyber security field, which is based on character feature,Bi-LSTM and conditional random field(CRF). The neural network model is for sequence labeling and CRF is for label dependency constraint,which then improves the accuracy of sequence labeling. Furthermore,as for datasets with the insufficient labeled samples in cyber security field,the proposed active learning method is able to achieve better sequence labeling effect with a small number of labeled samples.