首页|MTLAT: A Multi-Task Learning Framework Based on Adversarial Training for Chinese Cybersecurity NER
MTLAT: A Multi-Task Learning Framework Based on Adversarial Training for Chinese Cybersecurity NER
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NETL
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。
Yaopeng Han、Zhigang Lu、Bo Jiang、Yuling Liu、Chen Zhang、Zhengwei Jiang、Ning Li
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Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China,School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
IFIP WG 10.3 International Conference on Network and Parallel Computing
NETL
