Compara mètodes

	Classificació semi-supervisada basada en BERT	Classificació amb BERT basada en supervisió feble
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2019–2020	2017–2020
Autor original	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)
Tipus	Semi-supervised fine-tuning of pre-trained transformer	Weakly supervised fine-tuning of pre-trained language model
Font seminal	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.	Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017.
Àlies	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning
Relacionats	6	6
Resum	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.	Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling.