An R-Transformer_BiLSTM Model Based on Attention for Multi-label Text Classification-Reference-Cited by-同舟云学术

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An R-Transformer_BiLSTM Model Based on Attention for Multi-label Text Classification

Published:2022-06-24 Issue:2 Volume:55 Page:1293-1316
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Yan Yaoyao,Liu Fang’ai,Zhuang Xuqiang,Ju Jie

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-10938-y.pdf

Reference43 articles.

1. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE transactions on knowledge and data engineering 26(8):1819–1837

2. Bello M, Nápoles G, Sánchez R, Bello R, Vanhoof K (2020) Deep neural network to extract high-level features and labels in multi-label classification problems. Neurocomputing 413:259–270

3. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

4. Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359

5. Zhang M-L, Zhou Z-H (2007) Ml-knn: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

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