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Improving few-shot relation classification with multi-scale hierarchical prototype learning

delete2025-09-18
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PRE
AI
H
Haijia Bi
L
Liu, Lu
H
Hai Cui
S
Shengyue Liu
R
Ridong Han
J
Jiayu Han
彭涛 cover
彭涛 (Tao Peng)
DOI:10.1016/j.neunet.2025.108124delete
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Abstract

Abstract

En 中文
• Propose a multi-scale hierarchical prototype learning method for few-shot relation classification. • Integrate inter-set, inter-class, and intra-class interaction for better prototype representation. • Incorporate relational descriptions to emulate human cognition and enhance semantic understanding. • Alleviate data sparsity and improve classification by reducing distribution differences.

Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
8.2K
Citations:
3.0W

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U
University of Washington
Scholars:
8.0W
Papers: 7.0W
Citations: 12.5W
C
China University of Mining and Technology
Scholars:
8.6K
Papers: 3.1K
Citations: 3.1W
J
Jilin University
Scholars:
8.7W
Papers: 5.6W
Citations: 8.9K
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