방법 비교

	설명 가능한 LDA 토픽 모델	잠재 디리클레 할당 (Latent Dirichlet Allocation, LDA)
분야	딥러닝	머신러닝
계열	Machine learning	Latent structure
기원 연도	2003 (LDA); 2018–present (explainability extensions)	2003
창시자	Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
유형	Probabilistic generative topic model with interpretability enhancements	Generative probabilistic topic model (three-level hierarchical Bayesian)
원전	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
별칭	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
관련	4	3
요약	Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.	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.