{"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/false-positive-and-cross-relation-signals-in","title":"False Positive and Cross-relation Signals in Distant Supervision Data","arxiv_id":"1711.05186","date":"2017-11-14","proceeding":null,"authors":["Anca Dumitrache","Lora Aroyo","Chris Welty"],"abstract":"Distant supervision (DS) is a well-established method for relation extraction\nfrom text, based on the assumption that when a knowledge-base contains a\nrelation between a term pair, then sentences that contain that pair are likely\nto express the relation. In this paper, we use the results of a crowdsourcing\nrelation extraction task to identify two problems with DS data quality: the\nwidely varying degree of false positives across different relations, and the\nobserved causal connection between relations that are not considered by the DS\nmethod. The crowdsourcing data aggregation is performed using ambiguity-aware\nCrowdTruth metrics, that are used to capture and interpret inter-annotator\ndisagreement. We also present preliminary results of using the crowd to enhance\nDS training data for a relation classification model, without requiring the\ncrowd to annotate the entire set.","url_abs":"http://arxiv.org/abs/1711.05186v2","url_pdf":"http://arxiv.org/pdf/1711.05186v2.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":"false-positive-and-cross-relation-signals-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","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"}],"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}