Salīdzināt metodes

	Pielāgotais LDA tēmu modelis	Klasifikācija, kas pielāgota ar BERT
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2003 (base); adaptation practice ~2010s	2019
Autors	Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
Tips	Probabilistic generative topic model (fine-tuned / domain-adapted)	Pre-trained transformer fine-tuned for classification
Pirmavots	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, 4171–4186.
Citi nosaukumi	Domain-Adapted LDA, Adapted LDA, LDA Fine-Tuning, Online LDA Fine-Tuning	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Saistītās	5	5
Kopsavilkums	Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather than fitting LDA from scratch, the pre-trained topic-word distributions are used as an informed starting point, enabling the model to discover coherent domain topics faster and with less data than training cold.	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.