Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning

被引：11

作者：

Zhang, Han ^{[1
,2
]}

Guo, Yuanbo ^{[1
]}

Li, Tao ^{[1
]}

机构：

[1] Informat Engn Univ, Zhengzhou, Henan, Peoples R China

[2] Zhengzhou Univ, Zhengzhou, Henan, Peoples R China

来源：

SECURITY AND COMMUNICATION NETWORKS | 2019年

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1155/2019/6417407

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to obtain high quality and large-scale labelled data for information security research, we propose a new approach that combines a generative adversarial network with the BiLSTM-Attention-CRF model to obtain labelled data from crowd annotations. We use the generative adversarial network to find common features in crowd annotations and then consider them in conjunction with the domain dictionary feature and sentence dependency feature as additional features to be introduced into the BiLSTM-Attention-CRF model, which is then used to carry out named entity recognition in crowdsourcing. Finally, we create a dataset to evaluate our models using information security data. The experimental results show that our model has better performance than the other baseline models.

引用

页数：9