{"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/crowdsourcing-semantic-label-propagation-in","title":"Crowdsourcing Semantic Label Propagation in Relation Classification","arxiv_id":"1809.00537","date":"2018-09-03","proceeding":"WS 2018 11","authors":["Anca Dumitrache","Lora Aroyo","Chris Welty"],"abstract":"Distant supervision is a popular method for performing relation extraction\nfrom text that is known to produce noisy labels. Most progress in relation\nextraction and classification has been made with crowdsourced corrections to\ndistant-supervised labels, and there is evidence that indicates still more\nwould be better. In this paper, we explore the problem of propagating human\nannotation signals gathered for open-domain relation classification through the\nCrowdTruth methodology for crowdsourcing, that captures ambiguity in\nannotations by measuring inter-annotator disagreement. Our approach propagates\nannotations to sentences that are similar in a low dimensional embedding space,\nexpanding the number of labels by two orders of magnitude. Our experiments show\nsignificant improvement in a sentence-level multi-class relation classifier.","url_abs":"http://arxiv.org/abs/1809.00537v1","url_pdf":"http://arxiv.org/pdf/1809.00537v1.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":"crowdsourcing-semantic-label-propagation-in","repo_url":"https://github.com/CrowdTruth/Open-Domain-Relation-Extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.00537","atlas_url":"https://app.syntology.ai/?focus=1809.00537","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}