Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Modelimi i temave të shpjegueshëm× | Modelimi i temave× | |
|---|---|---|
| Fusha | Mësimi i thellë | Mësimi i thellë |
| Familja | Machine learning | Machine learning |
| Viti i origjinës≠ | 2003–2020s | 1999–2003 |
| Krijuesi≠ | Community practice (Blei et al. seminal; explainability extensions 2010s–present) | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Lloji≠ | Unsupervised topic discovery + interpretability layer | Unsupervised generative probabilistic model |
| Burimi themelues | 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. link ↗ |
| Emërtime të tjera | XTM, interpretable topic modeling, transparent topic modeling, explainable LDA | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Të lidhura≠ | 6 | 5 |
| Përmbledhja≠ | Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
| ScholarGateSeti i të dhënave ↗ |
|
|