Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pusuzraudzītā LDA tēmu modelis× | Daļēji uzraudzīts NMF tēmu modelis× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2009 | 2001 (NMF); semi-supervised variants from ~2010s |
| Autors≠ | Ramage, D.; Andrzejewski, D. et al. | Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others |
| Tips≠ | Semi-supervised probabilistic topic model | Matrix factorization with supervision |
| Pirmavots≠ | 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 ↗ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ |
| Citi nosaukumi | Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA | SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. | Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora. |
| ScholarGateDatu kopa ↗ |
|
|