השוואת שיטות
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| מודל נושאים LDA מונחה-למחצה× | מודל נושאים NMF חצי-מפוקח× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2009 | 2001 (NMF); semi-supervised variants from ~2010s |
| הוגה השיטה≠ | Ramage, D.; Andrzejewski, D. et al. | Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others |
| סוג≠ | Semi-supervised probabilistic topic model | Matrix factorization with supervision |
| מקור מכונן≠ | 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 ↗ |
| כינויים | Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA | SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF |
| קשורות | 6 | 6 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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