Linganisha mbinu

	Urekebishaji wa Kikoa	Uchanganuzi wa Hisia	Kujifunza kwa uhamishaji
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Ujifunzaji wa Mashine
Familia	Process / pipeline	Process / pipeline	Machine learning
Mwaka wa asili	—	—	2010 (formalized); 1990s (early roots)
Mwanzilishi	—	—	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Aina	NLP transfer-learning / fine-tuning pipeline	NLP text-classification task	Learning paradigm
Chanzo asilia	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Majina mbadala	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	opinion mining, polarity detection, duygu analizi	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Zinazohusiana	4	3	3
Muhtasari	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.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	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.