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| 自己教師ありトピックモデリング× | 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≠ | 2020–2023 | 2003 |
| 提唱者≠ | Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023) | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| 種類≠ | Self-supervised neural topic model | Probabilistic generative topic model |
| 原典≠ | Wu, X., Li, C., Zhu, Y., & Miao, Y. (2023). Effective Neural Topic Modeling with Embedding Clustering Regularization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, 37335–37357. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| 別名 | SSL topic model, self-supervised neural topic model, contrastive topic modeling, self-supervised LM-based topic modeling | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| 関連 | 5 | 5 |
| 概要≠ | Self-supervised topic modeling combines the interpretable topic discovery of classical topic models with self-supervised learning objectives — such as contrastive loss, masked language modeling, or reconstruction — to learn coherent, semantically rich topics from unlabeled text without human-annotated labels. It bridges classical probabilistic topic models and modern representation learning, yielding topics better aligned with contextual meaning. | Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words. |
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