{"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-browsing-for-research-papers-with","title":"Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis","arxiv_id":"1609.09188","date":"2016-09-29","proceeding":null,"authors":["Leonard K. M. Poon","Nevin L. Zhang"],"abstract":"Academic researchers often need to face with a large collection of research\npapers in the literature. This problem may be even worse for postgraduate\nstudents who are new to a field and may not know where to start. To address\nthis problem, we have developed an online catalog of research papers where the\npapers have been automatically categorized by a topic model. The catalog\ncontains 7719 papers from the proceedings of two artificial intelligence\nconferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet\nAllocation, we use a recently proposed method called hierarchical latent tree\nanalysis for topic modeling. The resulting topic model contains a hierarchy of\ntopics so that users can browse the topics from the top level to the bottom\nlevel. The topic model contains a manageable number of general topics at the\ntop level and allows thousands of fine-grained topics at the bottom level. It\nalso can detect topics that have emerged recently.","url_abs":"http://arxiv.org/abs/1609.09188v1","url_pdf":"http://arxiv.org/pdf/1609.09188v1.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-browsing-for-research-papers-with","repo_url":"https://github.com/kmpoon/hlta","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}