So sánh phương pháp

	Mô hình Chủ đề NMF Bán giám sát	Nhúng câu (Sentence Embeddings)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); semi-supervised variants from ~2010s	2015–2019
Người khởi xướng	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Loại	Matrix factorization with supervision	Representation learning / embedding
Công trình gốc	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.
Tên gọi khác	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Liên quan	6	4
Tóm tắt	Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.