{"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/dirichlet-belief-networks-for-topic-structure","title":"Dirichlet belief networks for topic structure learning","arxiv_id":"1811.00717","date":"2018-11-02","proceeding":"NeurIPS 2018 12","authors":["He Zhao","Lan Du","Wray Buntine","Mingyuan Zhou"],"abstract":"Recently, considerable research effort has been devoted to developing deep\narchitectures for topic models to learn topic structures. Although several deep\nmodels have been proposed to learn better topic proportions of documents, how\nto leverage the benefits of deep structures for learning word distributions of\ntopics has not yet been rigorously studied. Here we propose a new multi-layer\ngenerative process on word distributions of topics, where each layer consists\nof a set of topics and each topic is drawn from a mixture of the topics of the\nlayer above. As the topics in all layers can be directly interpreted by words,\nthe proposed model is able to discover interpretable topic hierarchies. As a\nself-contained module, our model can be flexibly adapted to different kinds of\ntopic models to improve their modelling accuracy and interpretability.\nExtensive experiments on text corpora demonstrate the advantages of the\nproposed model.","url_abs":"http://arxiv.org/abs/1811.00717v1","url_pdf":"http://arxiv.org/pdf/1811.00717v1.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":"dirichlet-belief-networks-for-topic-structure","repo_url":"https://github.com/ethanhezhao/DirBN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dirichlet-belief-networks-for-topic-structure","repo_url":"https://github.com/Fred-Chung/UBD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.00717","atlas_url":"https://app.syntology.ai/?focus=1811.00717","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}