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- Title
Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning.
- Authors
Zhang, Han; Guo, Yuanbo; Li, Tao
- Abstract
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.
- Subjects
INFORMATION technology security; CROWDSOURCING; ALGORITHMS; CONTRACTING out; ALGEBRA
- Publication
Security & Communication Networks, 2019, p1
- ISSN
1939-0114
- Publication type
Article
- DOI
10.1155/2019/6417407