基于深度主动学习的信息安全领域命名实体识别研究
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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.
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.
引文
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[17] Finkel J R,Grenager T,Manning C.Incorporating non-local information into information extraction systems by gibbs sampling [C]//Proceedings of the 43rd annual meeting on association for computational linguistics.Stroudsburg:ACM Press,2005.引用本文格式:中文:彭嘉毅,方勇,黄诚,等.基于深度主动学习的信息安全领域命名实体识别研究 [J].四川大学学报:自然科学版,2019,56:457.英文:Peng J Y,Fang Y,Huang C,et al.Cyber security named entity recognition based on deep active learning [J].J Sichuan Univ:Nat Sci Ed,2019,56:457.
[2] 冯艳红,于红,孙庚,等.基于词向量和条件随机场的领域术语识别方法 [J].计算机应用,2016,36:3146.
[3] Chiu J P C,Nichols E.Named entity recognition with bidirectional LSTM-CNNs [J/OL].Comput Sci,(2015-11-26) [2018-03-25].https://arxiv.org/abs/1511.08308.
[4] 邬伦,刘磊,李浩然,等.基于条件随机场的中文地名识别方法 [J].武汉大学学报:信息科学版,2017,42:150.
[5] 孙娟娟,于红,冯艳红,等.基于深度学习的渔业领域命名实体识别 [J].大连海洋大学学报,2018,33:265.
[6] 娄亮,周安民.基于主动学习CRF的信息安全领域命名实体识别研究 [J].通信与信息技术,2016,46:61.
[7] 吴思竹,钱庆,胡铁军,等.词形还原方法及实现工具比较分析 [J].数据分析与知识发现,2012,28:27.
[8] Bird S,Loper E.NLTK:the natural language toolkit [C]//Proceedings of the ACL 2004 on Interactive poster and demonstration sessions.Stroudsburg:ACM Press.2004.
[9] Pennington J,Socher R,Manning C.Glove:Global vectors for word representation [C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).[s.l.]:EMNLP,2014.
[10] Hochreiter S,Schmidhuber J.Long short-term memory [J].Neural Comput,1997,9:1735.
[11] 杨可心,桑永胜.基于BP神经网络的DDoS攻击检测研究 [J].四川大学学报:自然科学版,2017,54:71.
[12] 李勤,师维,孙界平,等.基于卷积神经网络的网络流量识别技术研究 [J].四川大学学报:自然科学版,2017,54:959.
[13] Schuster M,Paliwal K K.Bidirectional recurrent neural networks [J].IEEE Process Soc,1997,45:2673.
[14] Graves A,Schmidhuber J.Framewise phoneme classification with bidirectional LSTM and other neural network architectures.[J].Neural Networks,2005,18:602.
[15] 刘康,钱旭,王自强.主动学习算法综述 [J].计算机工程与应用,2012,48:1.
[16] 胡峰,周耀,王蕾.基于邻域粗糙集的主动学习方法 [J].重庆邮电大学学报:自然科学版,2017,29:776.
[17] Finkel J R,Grenager T,Manning C.Incorporating non-local information into information extraction systems by gibbs sampling [C]//Proceedings of the 43rd annual meeting on association for computational linguistics.Stroudsburg:ACM Press,2005.引用本文格式:中文:彭嘉毅,方勇,黄诚,等.基于深度主动学习的信息安全领域命名实体识别研究 [J].四川大学学报:自然科学版,2019,56:457.英文:Peng J Y,Fang Y,Huang C,et al.Cyber security named entity recognition based on deep active learning [J].J Sichuan Univ:Nat Sci Ed,2019,56:457.