Salīdzināt metodes

	Skaidrojama tēmu modelēšana	LDA tēmu modelis
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2003–2020s	2003
Autors	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Tips	Unsupervised topic discovery + interpretability layer	Probabilistic generative topic model
Pirmavots	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.
Citi nosaukumi	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Saistītās	6	5
Kopsavilkums	Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.