Võrdle meetodeid

	Eneseteadlik teemamodelleerimine	Poolt juhendatud teemamodelleerimine
Valdkond	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2020–2023	2009
Looja	Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)	Ramage, D.; Andrzejewski, D.; and related NLP community
Tüüp	Self-supervised neural topic model	Probabilistic graphical model (supervised/constrained extension of LDA)
Algallikas	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.
Rööpnimetused	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
Seotud	5	3
Kokkuvõte	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.