Comparar métodos

	Modelo de Tópicos LDA Auto-supervisado	Modelo de Tópicos LDA Semi-supervisado
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2003 (LDA); self-supervised variants from 2020	2009
Autor original	Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s)	Ramage, D.; Andrzejewski, D. et al.
Tipo	Probabilistic generative model with self-supervised pretraining	Semi-supervised probabilistic topic model
Fuente seminal	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	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.
Alias	SSL-LDA, self-supervised topic modeling, self-supervised LDA, contrastive LDA	Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA
Relacionados	6	6
Resumen	Self-supervised LDA combines the probabilistic generative framework of Latent Dirichlet Allocation with self-supervised pretraining signals — such as masked-word prediction or contrastive document objectives — to guide topic discovery without requiring hand-labeled training data. The result is topic representations that are simultaneously grounded in distributional statistics and enriched by language structure learned from raw text.	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.