Semi-supervised NMF Topic Model
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.
Rekod sumber
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- Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. · URL
- Jagarlamudi, J., Daume, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), 204–213. · URL
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