Sammenlign metoder

	Finjusteret Doc2Vec	Finjusteret Word2Vec
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2014 (base); fine-tuning practice ca. 2015	2013 (Word2Vec); fine-tuning practice 2014–2016
Ophavsperson	Le, Q. V. & Mikolov, T. (Doc2Vec base); fine-tuning practice adopted by the NLP community ca. 2015–2017	Mikolov, T. et al. (Word2Vec); fine-tuning practice generalised by the NLP community post-2013
Type	Representation learning / transfer learning	Domain-adapted word embedding model
Oprindelig kilde	Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR 2013 Workshop.
Aliasser	fine-tuned Paragraph Vector, domain-adapted Doc2Vec, PV fine-tuning, Doc2Vec transfer learning	domain-adapted Word2Vec, continued-training Word2Vec, Word2Vec fine-tuning, W2V domain adaptation
Relaterede	5	6
Resumé	Fine-Tuned Doc2Vec adapts a pre-trained Paragraph Vector (Doc2Vec) model by continuing its training on a target corpus, producing document embeddings that capture both the general language knowledge of the original training and the vocabulary and style of the new domain. It is used for text classification, semantic similarity, and clustering when labeled data are scarce but unlabeled domain text is available.	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.