手法を比較
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| 自己教師ありLDAトピックモデル× | 半教師ありLDAトピックモデル× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2003 (LDA); self-supervised variants from 2020 | 2009 |
| 提唱者≠ | Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s) | Ramage, D.; Andrzejewski, D. et al. |
| 種類≠ | Probabilistic generative model with self-supervised pretraining | Semi-supervised probabilistic topic model |
| 原典≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. 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 EMNLP, 248–256. link ↗ |
| 別名 | SSL-LDA, self-supervised topic modeling, self-supervised LDA, contrastive LDA | Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA |
| 関連 | 6 | 6 |
| 概要≠ | Self-supervised LDA combines the probabilistic generative framework of Latent Dirichlet Allocation with self-supervised pretraining signals — such as masked-word prediction or contrastive document objectives — to guide topic discovery without requiring hand-labeled training data. The result is topic representations that are simultaneously grounded in distributional statistics and enriched by language structure learned from raw text. | 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. |
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