So sánh phương pháp

	Mô hình chủ đề có giám sát yếu	Mô hình hóa chủ đề bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2012–2017	2009
Người khởi xướng	Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx)	Ramage, D.; Andrzejewski, D.; and related NLP community
Loại	Weakly supervised probabilistic topic model	Probabilistic graphical model (supervised/constrained extension of LDA)
Công trình gốc	Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of EACL 2012, 204–213.	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 the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics.
Tên gọi khác	guided topic modeling, seed-guided topic model, constrained topic modeling, seeded LDA	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model
Liên quan	5	3
Tóm tắt	Weakly supervised topic modeling incorporates lightweight domain knowledge — typically seed words or soft constraints — into a probabilistic topic model to steer discovered topics toward researcher-meaningful themes. It sits between fully unsupervised LDA and supervised classifiers, requiring far less annotation than the latter while producing more interpretable and domain-aligned topics than the former.	Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength.