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	Półnadzorowany model tematyczny LDA	Klasyfikacja oparta na BERT
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2009	2019
Twórca	Ramage, D.; Andrzejewski, D. et al.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Typ	Semi-supervised probabilistic topic model	Pre-trained language model with fine-tuning
Źródło pierwotne	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 EMNLP, 248–256.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Inne nazwy	Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Pokrewne	6	4
Podsumowanie	Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.