방법 비교

	약한 지도 학습 Word2Vec	약한 지도 하의 문장 임베딩
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2013–2016	2016–2019
창시자	Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al.	Ratner et al. (weak supervision framework); Reimers & Gurevych (sentence embeddings)
유형	Word embedding with noisy/programmatic labels	Representation learning under weak supervision
원전	Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26.	Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.
별칭	WS-Word2Vec, weakly-supervised word embeddings, weak-label Word2Vec, semi-noisy Word2Vec	WS sentence embeddings, noisy-label sentence representation learning, weakly supervised sentence representation, distant-supervision sentence embeddings
관련	6	6
요약	Weakly Supervised Word2Vec trains Word2Vec-style embeddings using automatically generated, noisy, or heuristic labels rather than costly manual annotation. By leveraging labeling functions, distant supervision, or keyword-based rules to assign soft labels, the approach enables domain-adapted word representations even when large manually annotated corpora are unavailable.	Weakly supervised sentence embeddings train dense sentence representations using noisy, heuristic, or programmatically generated labels instead of costly human annotation. Labeling functions — rules, distant supervision signals, or lightweight classifiers — supply approximate supervision that a label model aggregates into probabilistic labels, which then guide the sentence encoder to produce task-useful representations at scale.