Sammenlign metoder

	Overførsel af læring med Word2Vec	Finjusteret Word2Vec
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2013-2014	2013 (Word2Vec); fine-tuning practice 2014–2016
Ophavsperson	Mikolov, T. et al. (Word2Vec); transfer-learning application popularised by Kim, Y.	Mikolov, T. et al. (Word2Vec); fine-tuning practice generalised by the NLP community post-2013
Type	Transfer learning / embedding initialization	Domain-adapted word embedding model
Oprindelig kilde	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.	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR 2013 Workshop.
Aliasser	Word2Vec transfer learning, pre-trained Word2Vec embeddings, Word2Vec embedding initialization, Word2Vec fine-tuning	domain-adapted Word2Vec, continued-training Word2Vec, Word2Vec fine-tuning, W2V domain adaptation
Relaterede	5	6
Resumé	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.	Fine-Tuned Word2Vec adapts a pre-trained Word2Vec model to a specific domain or task by continuing its training on domain-specific text. Rather than training embeddings from scratch, practitioners load general-purpose vectors (e.g., Google News embeddings) and run additional Skip-gram or CBOW epochs on domain corpora, shifting word representations toward domain-specific usage patterns.