So sánh phương pháp

	Word2Vec bán giám sát	Phân loại dựa trên BERT bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2013–2015	2019–2020
Người khởi xướng	Mikolov, T. et al. (Word2Vec); semi-supervised framing via Collobert & Weston and subsequent NLP literature	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)
Loại	Semi-supervised representation learning	Semi-supervised fine-tuning of pre-trained transformer
Công trình gốc	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In Proceedings of ICLR 2013.	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.
Tên gọi khác	Word2Vec with semi-supervised learning, semi-supervised word embeddings, Word2Vec SSL, unsupervised pretraining with Word2Vec	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning
Liên quan	6	6
Tóm tắt	Semi-supervised Word2Vec trains dense word representations on a large unlabeled corpus using Word2Vec (skip-gram or CBOW), then uses those embeddings as fixed or fine-tunable input features for a downstream classifier trained on a small labeled dataset. This two-stage process lets models benefit from abundant unlabeled text when labeled data is scarce.	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.