Prototypical attention network for few-shot relation classification with entity-aware embedding module
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03677-z.pdf
Reference38 articles.
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3. Chen Q, Zhu X, Ling Z, Wei S, Jiang H, Inkpen D (2017) Enhanced LSTM for natural language inference. In: Proceedings of the 55th annual meeting of the association for computational linguistics. ACL, Stroudsburg, pp 1657–1668
4. Elsken T, Staffler B, Metzen J H, Hutter F (2020) Meta-learning of neural architectures for few-shot learning. In: Proceedings of the 33rd IEEE conference on computer vision and pattern recognition. IEEE, Piscataway, pp 12362–12372
5. Fan M, Bai Y, Sun M, Li P (2019) Large margin prototypical network for few-shot relation classification with fine-grained features. In: Proceedings of the 28th ACM international conference on information and knowledge management. ACM, New York, pp 2353–2356
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1. CoFF-CHP: coarse-to-fine filters with concept heuristic prompt for few-shot relation classification;Applied Intelligence;2024-07-02
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