{"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/exposing-ambiguities-in-a-relation-extraction","title":"Exposing ambiguities in a relation-extraction gold standard with crowdsourcing","arxiv_id":"1505.06256","date":"2015-05-23","proceeding":null,"authors":["Tong Shu Li","Benjamin M. Good","Andrew I. Su"],"abstract":"Semantic relation extraction is one of the frontiers of biomedical natural\nlanguage processing research. Gold standards are key tools for advancing this\nresearch. It is challenging to generate these standards because of the high\ncost of expert time and the difficulty in establishing agreement between\nannotators. We implemented and evaluated a microtask crowdsourcing approach\nthat can produce a gold standard for extracting drug-disease relations. The\naggregated crowd judgment agreed with expert annotations from a pre-existing\ncorpus on 43 of 60 sentences tested. The levels of crowd agreement varied in a\nsimilar manner to the levels of agreement among the original expert annotators.\nThis work rein-forces the power of crowdsourcing in the process of assembling\ngold standards for relation extraction. Further, it high-lights the importance\nof exposing the levels of agreement between human annotators, expert or crowd,\nin gold standard corpora as these are reproducible signals indicating\nambiguities in the data or in the annotation guidelines.","url_abs":"http://arxiv.org/abs/1505.06256v1","url_pdf":"http://arxiv.org/pdf/1505.06256v1.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":"exposing-ambiguities-in-a-relation-extraction","repo_url":"https://github.com/SuLab/crowdflower_relation_verification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"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}