{"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/constrained-relational-topic-models","title":"Constrained Relational Topic Models","arxiv_id":null,"date":"2020-02-01","proceeding":null,"authors":["Silvia Terragni","Elisabetta Fersini","Enza Messina"],"abstract":"Relational topic models (RTM) have been widely used to discover hidden topics in a collection of networked documents. In this paper, we introduce the class of Constrained Relational Topic Models (CRTM), a semi-supervised extension of RTM that, apart from modeling the structure of the document network, explicitly models some available domain knowledge. We propose two instances of CRTM that incorporate prior knowledge in the form of document constraints. The models smooth the probability distribution of topics such that two constrained documents can either share the same topics or denote distinct themes. Experimental results on benchmark relational datasets show significant performances of CRTM on a semi-supervised document classification task.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0020025519308850","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S0020025519308850","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":"constrained-relational-topic-models","repo_url":"https://github.com/MIND-Lab/Constrained-RTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}