方法对比
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| 弱监督LDA主题模型× | NMF 主题模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009–2012 | 1999 |
| 提出者≠ | Jagarlamudi et al.; Andrzejewski et al. | Lee, D. D. & Seung, H. S. |
| 类型≠ | Probabilistic generative model with weak supervision | Matrix factorization / unsupervised topic model |
| 开创性文献≠ | 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 ↗ |
| 别名 | WS-LDA, Guided LDA, Seeded LDA, Constrained LDA | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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