Σύγκριση μεθόδων

	Προσαρμογή Πεδίου	Ταξινόμηση Κειμένου	Εκμάθηση μεταφοράς
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου	Μηχανική Μάθηση
Οικογένεια	Process / pipeline	Process / pipeline	Machine learning
Έτος προέλευσης	—	—	2010 (formalized); 1990s (early roots)
Δημιουργός	—	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Τύπος	NLP transfer-learning / fine-tuning pipeline	Supervised NLP classification task	Learning paradigm
Θεμελιώδης πηγή	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Εναλλακτικές ονομασίες	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	text categorization, document classification, topic classification, metin sınıflandırma	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Συναφείς	4	4	3
Σύνοψη	Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.