%0 Conference Proceedings
%T MTLAT: A Multi-Task Learning Framework Based on Adversarial Training for Chinese Cybersecurity NER
%+ Institute of Information Engineering [Beijing] (IIE)
%+ School of Cyber Security
%A Han, Yaopeng
%A Lu, Zhigang
%A Jiang, Bo
%A Liu, Yuling
%A Zhang, Chen
%A Jiang, Zhengwei
%A Li, Ning
%Z Part 2: AI
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th IFIP International Conference on Network and Parallel Computing (NPC)
%C Zhengzhou, China
%Y Xin He
%Y En Shao
%Y Guangming Tan
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-12639
%P 43-54
%8 2020-09-28
%D 2020
%R 10.1007/978-3-030-79478-1_4
%K Cybersecurity
%K Named entity recognition
%K Adversarial training
%K Multi-task learning
%Z Computer Science [cs]Conference papers
%X With the continuous development of cybersecurity texts, the importance of Chinese cybersecurity named entity recognition (NER) is increasing. However, Chinese cybersecurity texts contain not only a large number of professional security domain entities but also many English person and organization entities, as well as a large number of Chinese-English mixed entities. Chinese Cybersecurity NER is a domain-specific task, current models rarely focus on the cybersecurity domain and cannot extract these entities well. To tackle these issues, we propose a Multi-Task Learning framework based on Adversarial Training (MTLAT) to improve the performance of Chinese cybersecurity NER. Extensive experimental results show that our model, which does not use any external resources except static word embedding, outperforms state-of-the-art systems on the Chinese cybersecurity dataset. Moreover, our model outperforms the BiLSTM-CRF method on Weibo, Resume, and MSRA Chinese general NER datasets by 4.1%, 1.04%, 1.79% F1 scores, which proves the universality of our model in different domains.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03768765/document
%2 https://inria.hal.science/hal-03768765/file/511910_1_En_4_Chapter.pdf
%L hal-03768765
%U https://inria.hal.science/hal-03768765
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-12639