摘要
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.
作者
彭嘉毅
方勇
黄诚
刘亮
姜政伟
PENG Jia-Yi;FANG Yong;HUANG Cheng;LIU Liang;JIANG Zheng-Wei(College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China;College of Cybersecurity, Sichuan University, Chengdu 610065, China;Key Laboratory of Network Assessment Technology, CAS (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093, China)
出处
《四川大学学报(自然科学版)》
CAS
CSCD
北大核心
2019年第3期457-462,共6页
Journal of Sichuan University(Natural Science Edition)
基金
中国科学院网络测评技术重点实验室开放课题基金(NST-18-001)