Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Semiveiledetet NMF-emnemodell× | Setningsembddinger× | |
|---|---|---|
| Fagfelt | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2001 (NMF); semi-supervised variants from ~2010s | 2015–2019 |
| Opphavsperson≠ | Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others | Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019) |
| Type≠ | Matrix factorization with supervision | Representation learning / embedding |
| Opprinnelig kilde≠ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ | 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. DOI ↗ |
| Alias | SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF | sentence vectors, sentence representations, SBERT, semantic sentence encoding |
| Relaterte≠ | 6 | 4 |
| Sammendrag≠ | 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. |
| ScholarGateDatasett ↗ |
|
|