So sánh phương pháp

	Mô hình hóa chủ đề bán giám sát	Phân bổ Dirichlet ẩn (LDA)
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Latent structure
Năm ra đời	2009	2003
Người khởi xướng	Ramage, D.; Andrzejewski, D.; and related NLP community	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Loại	Probabilistic graphical model (supervised/constrained extension of LDA)	Generative probabilistic topic model (three-level hierarchical Bayesian)
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 the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Tên gọi khác	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Liên quan	3	3
Tóm tắt	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.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.