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| Domeenipõhine Word2Vec× | Ülekandeõpe Word2Veciga× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2013–2016 | 2013-2014 |
| Looja≠ | Mikolov, T. et al. (Word2Vec); domain adaptation practice emerged in NLP community ~2014–2016 | Mikolov, T. et al. (Word2Vec); transfer-learning application popularised by Kim, Y. |
| Tüüp≠ | Domain-adapted word embedding model | Transfer learning / embedding initialization |
| Algallikas≠ | Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR Workshop. link ↗ | Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (NIPS), 26, 3111-3119. link ↗ |
| Rööpnimetused | domain-specific Word2Vec, domain-adapted word embeddings, domain Word2Vec, specialized Word2Vec | Word2Vec transfer learning, pre-trained Word2Vec embeddings, Word2Vec embedding initialization, Word2Vec fine-tuning |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Domain-adaptive Word2Vec trains or fine-tunes Word2Vec embeddings on a domain-specific text corpus so that word vectors capture the specialized vocabulary, semantic relationships, and jargon of a target field — such as clinical medicine, legal text, financial reports, or scientific literature — rather than reflecting general-purpose web or news language. | Transfer Learning with Word2Vec uses word embeddings pre-trained on large text corpora via the Skip-gram or CBOW objectives introduced by Mikolov et al. (2013) to initialize the embedding layer of a downstream NLP model. This approach transfers distributional semantic knowledge to tasks where labeled data is scarce, consistently outperforming random initialization. |
| ScholarGateAndmestik ↗ |
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