Sammenlign metoder

	Forklarlig emnemodellering	Forklarbar BERT-baseret klassifikation
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2003–2020s	2019–2020
Ophavsperson	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Type	Unsupervised topic discovery + interpretability layer	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Oprindelig kilde	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
Aliasser	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Relaterede	6	6
Resumé	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.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.