Порівняння методів

	Самокероване тематичне моделювання	Напівкероване моделювання тем
Галузь	Глибоке навчання	Глибоке навчання
Родина	Machine learning	Machine learning
Рік появи	2020–2023	2009
Автор методу	Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)	Ramage, D.; Andrzejewski, D.; and related NLP community
Тип	Self-supervised neural topic model	Probabilistic graphical model (supervised/constrained extension of LDA)
Основоположне джерело	Wu, X., Li, C., Zhu, Y., & Miao, Y. (2023). Effective Neural Topic Modeling with Embedding Clustering Regularization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, 37335–37357.	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.
Інші назви	SSL topic model, self-supervised neural topic model, contrastive topic modeling, self-supervised LM-based topic modeling	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model
Пов'язані	5	3
Підсумок	Self-supervised topic modeling combines the interpretable topic discovery of classical topic models with self-supervised learning objectives — such as contrastive loss, masked language modeling, or reconstruction — to learn coherent, semantically rich topics from unlabeled text without human-annotated labels. It bridges classical probabilistic topic models and modern representation learning, yielding topics better aligned with contextual meaning.	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.