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| 약지도 LDA 토픽 모델× | NMF 토픽 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009–2012 | 1999 |
| 창시자≠ | Jagarlamudi et al.; Andrzejewski et al. | Lee, D. D. & Seung, H. S. |
| 유형≠ | Probabilistic generative model with weak supervision | Matrix factorization / unsupervised topic model |
| 원전≠ | Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pp. 204–213. link ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| 별칭 | WS-LDA, Guided LDA, Seeded LDA, Constrained LDA | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| 관련≠ | 6 | 4 |
| 요약≠ | Weakly Supervised LDA is an extension of Latent Dirichlet Allocation that incorporates lightweight human guidance — typically keyword seeds or must-link/cannot-link constraints — into the Dirichlet priors, steering learned topics toward domain-meaningful themes without requiring fully labeled documents. It sits between fully unsupervised LDA and supervised classification, making it well-suited to situations where labeling thousands of documents is impractical. | Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics. |
| ScholarGate데이터셋 ↗ |
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