{"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/dapper-scaling-dynamic-author-persona-topic","title":"DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora","arxiv_id":"1811.01931","date":"2018-11-03","proceeding":null,"authors":["Robert Giaquinto","Arindam Banerjee"],"abstract":"Extracting common narratives from multi-author dynamic text corpora requires\ncomplex models, such as the Dynamic Author Persona (DAP) topic model. However,\nsuch models are complex and can struggle to scale to large corpora, often\nbecause of challenging non-conjugate terms. To overcome such challenges, in\nthis paper we adapt new ideas in approximate inference to the DAP model,\nresulting in the DAP Performed Exceedingly Rapidly (DAPPER) topic model.\nSpecifically, we develop Conjugate-Computation Variational Inference (CVI)\nbased variational Expectation-Maximization (EM) for learning the model,\nyielding fast, closed form updates for each document, replacing iterative\noptimization in earlier work. Our results show significant improvements in\nmodel fit and training time without needing to compromise the model's temporal\nstructure or the application of Regularized Variation Inference (RVI). We\ndemonstrate the scalability and effectiveness of the DAPPER model by extracting\nhealth journeys from the CaringBridge corpus --- a collection of 9 million\njournals written by 200,000 authors during health crises.","url_abs":"http://arxiv.org/abs/1811.01931v1","url_pdf":"http://arxiv.org/pdf/1811.01931v1.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":"dapper-scaling-dynamic-author-persona-topic","repo_url":"https://github.com/robert-giaquinto/dapper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}