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| Domeenipõhine Word2Vec× | Word2Vec× | |
|---|---|---|
| Valdkond≠ | Süvaõpe | Tekstikaeve |
| Perekond≠ | Machine learning | Process / pipeline |
| Tekkeaasta≠ | 2013–2016 | 2013 |
| Looja≠ | Mikolov, T. et al. (Word2Vec); domain adaptation practice emerged in NLP community ~2014–2016 | Tomas Mikolov et al. |
| Tüüp≠ | Domain-adapted word embedding model | Neural word-embedding model |
| 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., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. link ↗ |
| Rööpnimetused | domain-specific Word2Vec, domain-adapted word embeddings, domain Word2Vec, specialized Word2Vec | word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri |
| Seotud≠ | 5 | 4 |
| 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. | 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. |
| ScholarGateAndmestik ↗ |
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