So sánh phương pháp

	Mô hình Chủ đề NMF	Phân loại dựa trên BERT
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	1999	2019
Người khởi xướng	Lee, D. D. & Seung, H. S.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Loại	Matrix factorization / unsupervised topic model	Pre-trained language model with fine-tuning
Công trình gốc	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Tên gọi khác	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Liên quan	4	4
Tóm tắt	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.