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| Forklarlig LDA Emne Model× | Latent Dirichlet Allocation (LDA)× | |
|---|---|---|
| Fagområde≠ | Dyb læring | Maskinlæring |
| Familie≠ | Machine learning | Latent structure |
| Oprindelsesår≠ | 2003 (LDA); 2018–present (explainability extensions) | 2003 |
| Ophavsperson≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors | Blei, D. M.; Ng, A. Y.; Jordan, M. I. |
| Type≠ | Probabilistic generative topic model with interpretability enhancements | Generative probabilistic topic model (three-level hierarchical Bayesian) |
| Oprindelig kilde | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ |
| Aliasser≠ | Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model | LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery. | Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing. |
| ScholarGateDatasæt ↗ |
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