So sánh phương pháp

	Mô hình Chủ đề LDA Bán Giám sát	Mô hình Chủ đề NMF Bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2009	2001 (NMF); semi-supervised variants from ~2010s
Người khởi xướng	Ramage, D.; Andrzejewski, D. et al.	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others
Loại	Semi-supervised probabilistic topic model	Matrix factorization with supervision
Công trình gốc	Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of EMNLP, 248–256.	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.
Tên gọi khác	Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF
Liên quan	6	6
Tóm tắt	Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.	Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.