Comparar métodos

	Clasificación basada en BERT con supervisión débil	Clasificación semisupervisada basada en BERT
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2017–2020	2019–2020
Autor original	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)
Tipo	Weakly supervised fine-tuning of pre-trained language model	Semi-supervised fine-tuning of pre-trained transformer
Fuente seminal	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.	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.
Alias	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning
Relacionados	6	6
Resumen	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.	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.