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| Modello di argomento LDA semi-supervisionato× | Topic Modeling× | |
|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2009 | 1999–2003 |
| Ideatore≠ | Ramage, D.; Andrzejewski, D. et al. | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Tipo≠ | Semi-supervised probabilistic topic model | Unsupervised generative probabilistic model |
| Fonte seminale≠ | 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. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Alias | Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | 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. | 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. |
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