Trigger-free cybersecurity event detection based on contrastive learning

Author:

Tang Mengmeng,Guo Yuanbo,Bai Qingchun,Zhang Han

Funder

National Natural Science Foundation of China

Major Public Welfare Project of Henan Province

the open funds of NPPA Key Laboratory of Publishing Integration Development

ECNUP

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Reference37 articles.

1. Jian L, Yubo C, Kang L, Jun Z (2019) Neural cross-lingual event detection with minimal parallel resources. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP), Association for Computational Linguistics. pp. 738–748, Hong Kong, China.

2. Meihan T, Bin X, Shuai W, Yixin C, Lei H, Juanzi L, Jun X (2020) Improving event detection via open-domain trigger knowledge. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online. Association for Computational Linguistics, pp. 5887–5897.

3. Lifu H, Heng J (2020) Semi-supervised new event type induction and event detection. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online. Association for Computational Linguistics, pp. 718–724.

4. Christopher W, Stephanie S, Julie M, Kazuaki MACE (2005) multilingual training corpus. Linguistic Data Consortium, Philadelphia, 57.

5. Satyapanich T, Ferraro F, Finin T (2020) Casie: Extracting cybersecurity event information from text. In: Proceedings of the AAAI Conference on Artifificial Intelligence, pp. 8749–8757.

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