Сравнение методов

	Объяснимое тематическое моделирование	Тематическое моделирование
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2003–2020s	1999–2003
Автор метода	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Тип	Unsupervised topic discovery + interpretability layer	Unsupervised generative probabilistic model
Основополагающий источник	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.
Другие названия	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Связанные	6	5
Сводка	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.