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| 약한 지도 토픽 모델링× | 준지도 학습 토픽 모델링× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2012–2017 | 2009 |
| 창시자≠ | Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx) | Ramage, D.; Andrzejewski, D.; and related NLP community |
| 유형≠ | Weakly supervised probabilistic topic model | Probabilistic graphical model (supervised/constrained extension of LDA) |
| 원전≠ | Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of EACL 2012, 204–213. link ↗ | 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 the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics. link ↗ |
| 별칭 | guided topic modeling, seed-guided topic model, constrained topic modeling, seeded LDA | semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model |
| 관련≠ | 5 | 3 |
| 요약≠ | Weakly supervised topic modeling incorporates lightweight domain knowledge — typically seed words or soft constraints — into a probabilistic topic model to steer discovered topics toward researcher-meaningful themes. It sits between fully unsupervised LDA and supervised classifiers, requiring far less annotation than the latter while producing more interpretable and domain-aligned topics than the former. | Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength. |
| ScholarGate데이터셋 ↗ |
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