{"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/unsupervised-domain-adaptation-with-feature","title":"Unsupervised Domain Adaptation with Feature Embeddings","arxiv_id":"1412.4385","date":"2014-12-14","proceeding":null,"authors":["Yi Yang","Jacob Eisenstein"],"abstract":"Representation learning is the dominant technique for unsupervised domain\nadaptation, but existing approaches often require the specification of \"pivot\nfeatures\" that generalize across domains, which are selected by task-specific\nheuristics. We show that a novel but simple feature embedding approach provides\nbetter performance, by exploiting the feature template structure common in NLP\nproblems.","url_abs":"http://arxiv.org/abs/1412.4385v3","url_pdf":"http://arxiv.org/pdf/1412.4385v3.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":"unsupervised-domain-adaptation-with-feature","repo_url":"https://github.com/yiyang-gt/feat2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"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}