Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Wu, Ronga | Yu, Longb | Tian, Shengweic | Long, Jund | Zhou, Tiejune | Wang, Boa
Affiliations: [a] School of Software, University of Xinjiang, Xinjiang, China | [b] Network and Information Center, University of Xinjiang, Xinjiang, China | [c] Key Laboratory of Software Engineering Technology, University of Xinjiang, Xinjiang, China | [d] Institute of Big Data Research, University of Central South, Changsha, China | [e] Internet Information Security Centre, Xinjiang, China
Correspondence: [*] Corresponding author. Long Yu, Network and Information Center, University of Xinjiang, Xinjiang, China. E-mail: yul_xju@163.com
Abstract: Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time.
Keywords: Few-shot event detection, PA-CRF, Contrast Learning, Glove
DOI: 10.3233/JIFS-234368
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4265-4275, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl