{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/topic-supervised-non-negative-matrix","title":"Topic supervised non-negative matrix factorization","arxiv_id":"1706.05084","date":"2017-06-12","proceeding":null,"authors":["Kelsey MacMillan","James D. Wilson"],"abstract":"Topic models have been extensively used to organize and interpret the\ncontents of large, unstructured corpora of text documents. Although topic\nmodels often perform well on traditional training vs. test set evaluations, it\nis often the case that the results of a topic model do not align with human\ninterpretation. This interpretability fallacy is largely due to the\nunsupervised nature of topic models, which prohibits any user guidance on the\nresults of a model. In this paper, we introduce a semi-supervised method called\ntopic supervised non-negative matrix factorization (TS-NMF) that enables the\nuser to provide labeled example documents to promote the discovery of more\nmeaningful semantic structure of a corpus. In this way, the results of TS-NMF\nbetter match the intuition and desired labeling of the user. The core of TS-NMF\nrelies on solving a non-convex optimization problem for which we derive an\niterative algorithm that is shown to be monotonic and convergent to a local\noptimum. We demonstrate the practical utility of TS-NMF on the Reuters and\nPubMed corpora, and find that TS-NMF is especially useful for conceptual or\nbroad topics, where topic key terms are not well understood. Although\nidentifying an optimal latent structure for the data is not a primary objective\nof the proposed approach, we find that TS-NMF achieves higher weighted Jaccard\nsimilarity scores than the contemporary methods, (unsupervised) NMF and latent\nDirichlet allocation, at supervision rates as low as 10% to 20%.","url_abs":"http://arxiv.org/abs/1706.05084v2","url_pdf":"http://arxiv.org/pdf/1706.05084v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"topic-supervised-non-negative-matrix","repo_url":"https://github.com/Vokturz/tsnmf-sparse","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}