Salīdzināt metodes
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| Vāji uzraudzīts Word2Vec× | Daudzpusīgi paši apmācīti Word2Vec× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2013–2016 | 2013–2015 |
| Autors≠ | Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al. | Mikolov, T. et al. (Word2Vec); semi-supervised framing via Collobert & Weston and subsequent NLP literature |
| Tips≠ | Word embedding with noisy/programmatic labels | Semi-supervised representation learning |
| 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. link ↗ | Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In Proceedings of ICLR 2013. link ↗ |
| Citi nosaukumi | WS-Word2Vec, weakly-supervised word embeddings, weak-label Word2Vec, semi-noisy Word2Vec | Word2Vec with semi-supervised learning, semi-supervised word embeddings, Word2Vec SSL, unsupervised pretraining with Word2Vec |
| Saistītās | 6 | 6 |
| 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. | 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. |
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