Salīdzināt metodes

	Vāji uzraudzīts Word2Vec	Ievietojumi teikumiem
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2013–2016	2015–2019
Autors	Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al.	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Tips	Word embedding with noisy/programmatic labels	Representation learning / embedding
Pirmavots	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.	Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.
Citi nosaukumi	WS-Word2Vec, weakly-supervised word embeddings, weak-label Word2Vec, semi-noisy Word2Vec	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Saistītās	6	4
Kopsavilkums	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.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.