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| Heikosti valvottu Word2Vec× | Word2Vec× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Tekstinlouhinta |
| Menetelmäperhe≠ | Machine learning | Process / pipeline |
| Syntyvuosi≠ | 2013–2016 | 2013 |
| Kehittäjä≠ | Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al. | Tomas Mikolov et al. |
| Tyyppi≠ | Word embedding with noisy/programmatic labels | Neural word-embedding model |
| Alkuperäislähde≠ | 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. link ↗ |
| Rinnakkaisnimet | WS-Word2Vec, weakly-supervised word embeddings, weak-label Word2Vec, semi-noisy Word2Vec | word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri |
| Liittyvät≠ | 6 | 4 |
| Tiivistelmä≠ | 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. | Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically. |
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