{"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-grouper-an-agglomerative-clustering","title":"Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling","arxiv_id":"1904.06483","date":"2019-04-13","proceeding":null,"authors":["Daniel Pfeifer","Jochen L. Leidner"],"abstract":"We introduce Topic Grouper as a complementary approach in the field of\nprobabilistic topic modeling. Topic Grouper creates a disjunctive partitioning\nof the training vocabulary in a stepwise manner such that resulting partitions\nrepresent topics. It is governed by a simple generative model, where the\nlikelihood to generate the training documents via topics is optimized. The\nalgorithm starts with one-word topics and joins two topics at every step. It\ntherefore generates a solution for every desired number of topics ranging\nbetween the size of the training vocabulary and one. The process represents an\nagglomerative clustering that corresponds to a binary tree of topics. A\nresulting tree may act as a containment hierarchy, typically with more general\ntopics towards the root of tree and more specific topics towards the leaves.\nTopic Grouper is not governed by a background distribution such as the\nDirichlet and avoids hyper parameter optimizations.\n  We show that Topic Grouper has reasonable predictive power and also a\nreasonable theoretical and practical complexity. Topic Grouper can deal well\nwith stop words and function words and tends to push them into their own\ntopics. Also, it can handle topic distributions, where some topics are more\nfrequent than others. We present typical examples of computed topics from\nevaluation datasets, where topics appear conclusive and coherent. In this\ncontext, the fact that each word belongs to exactly one topic is not a major\nlimitation; in some scenarios this can even be a genuine advantage, e.g.~a\nrelated shopping basket analysis may aid in optimizing groupings of articles in\nsales catalogs.","url_abs":"http://arxiv.org/abs/1904.06483v1","url_pdf":"http://arxiv.org/pdf/1904.06483v1.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-grouper-an-agglomerative-clustering","repo_url":"https://github.com/pfeiferd/TopicGrouperJ","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}