{"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-model-supervised-by-understanding-map","title":"Topic Model Supervised by Understanding Map","arxiv_id":"2110.06043","date":"2021-10-12","proceeding":null,"authors":["Gangli Liu"],"abstract":"Inspired by the notion of Center of Mass in physics, an extension called Semantic Center of Mass (SCOM) is proposed, and used to discover the abstract \"topic\" of a document. The notion is under a framework model called Understanding Map Supervised Topic Model (UM-S-TM). The devise aim of UM-S-TM is to let both the document content and a semantic network -- specifically, Understanding Map -- play a role, in interpreting the meaning of a document. Based on different justifications, three possible methods are devised to discover the SCOM of a document. Some experiments on artificial documents and Understanding Maps are conducted to test their outcomes. In addition, its ability of vectorization of documents and capturing sequential information are tested. We also compared UM-S-TM with probabilistic topic models like Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA).","url_abs":"https://arxiv.org/abs/2110.06043v9","url_pdf":"https://arxiv.org/pdf/2110.06043v9.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":[],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"experiment-data-for-um-s-tm","name":"Experiment-data-for-UM-S-TM","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}