Knowledge and separating soft verbalizer based prompt-tuning for multi-label short text classification
Author:
Funder
Innovative Research Group Project of the National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05599-4.pdf
Reference43 articles.
1. Peng Z, Abdollahi B, Xie M, et al (2021) Multi-label classification of short texts with label correlated recurrent neural networks. In: Proceedings of the 2021 ACM SIGIR international conference on theory of information retrieval. pp 119–122
2. Lyu P, Rao G, Zhang L et al (2023) Bilgat: Bidirectional lattice graph attention network for Chinese short text classification. Appl Intell 1–10
3. Wang R, Long S, Dai X, et al (2021) Meta-lmtc: meta-learning for large-scale multi-label text classification. In: Proceedings of the 2021 conference on empirical methods in natural language processing. pp 8633–8646
4. Huang M, Zhuang F, Zhang X et al (2019) Supervised representation learning for multi-label classification. Mach Learn 108:747–763
5. Tarekegn AN, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recogn 118:107965
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