{"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/cross-domain-labeled-lda-for-cross-domain","title":"Cross-Domain Labeled LDA for Cross-Domain Text Classification","arxiv_id":"1809.05820","date":"2018-09-16","proceeding":null,"authors":["Baoyu Jing","Chenwei Lu","Deqing Wang","Fuzhen Zhuang","Cheng Niu"],"abstract":"Cross-domain text classification aims at building a classifier for a target\ndomain which leverages data from both source and target domain. One promising\nidea is to minimize the feature distribution differences of the two domains.\nMost existing studies explicitly minimize such differences by an exact\nalignment mechanism (aligning features by one-to-one feature alignment,\nprojection matrix etc.). Such exact alignment, however, will restrict models'\nlearning ability and will further impair models' performance on classification\ntasks when the semantic distributions of different domains are very different.\nTo address this problem, we propose a novel group alignment which aligns the\nsemantics at group level. In addition, to help the model learn better semantic\ngroups and semantics within these groups, we also propose a partial supervision\nfor model's learning in source domain. To this end, we embed the group\nalignment and a partial supervision into a cross-domain topic model, and\npropose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and\nReuters dataset, extensive quantitative (classification, perplexity etc.) and\nqualitative (topic detection) experiments are conducted to show the\neffectiveness of the proposed group alignment and partial supervision.","url_abs":"http://arxiv.org/abs/1809.05820v1","url_pdf":"http://arxiv.org/pdf/1809.05820v1.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":"cross-domain-labeled-lda-for-cross-domain","repo_url":"https://github.com/ZZy979/CDL-LDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"cross-domain-text-classification","task_name":"Cross-Domain Text Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.05820","atlas_url":"https://app.syntology.ai/?focus=1809.05820","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}