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Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zhang, Han& Guo, Yuanbo& Li, Tao. 2019. Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1210513
Modern Language Association (MLA)
Zhang, Han…[et al.]. Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning. Security and Communication Networks No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1210513
American Medical Association (AMA)
Zhang, Han& Guo, Yuanbo& Li, Tao. Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1210513
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210513