{"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/capturing-ambiguity-in-crowdsourcing-frame","title":"Capturing Ambiguity in Crowdsourcing Frame Disambiguation","arxiv_id":"1805.00270","date":"2018-05-01","proceeding":null,"authors":["Anca Dumitrache","Lora Aroyo","Chris Welty"],"abstract":"FrameNet is a computational linguistics resource composed of semantic frames,\nhigh-level concepts that represent the meanings of words. In this paper, we\npresent an approach to gather frame disambiguation annotations in sentences\nusing a crowdsourcing approach with multiple workers per sentence to capture\ninter-annotator disagreement. We perform an experiment over a set of 433\nsentences annotated with frames from the FrameNet corpus, and show that the\naggregated crowd annotations achieve an F1 score greater than 0.67 as compared\nto expert linguists. We highlight cases where the crowd annotation was correct\neven though the expert is in disagreement, arguing for the need to have\nmultiple annotators per sentence. Most importantly, we examine cases in which\ncrowd workers could not agree, and demonstrate that these cases exhibit\nambiguity, either in the sentence, frame, or the task itself, and argue that\ncollapsing such cases to a single, discrete truth value (i.e. correct or\nincorrect) is inappropriate, creating arbitrary targets for machine learning.","url_abs":"http://arxiv.org/abs/1805.00270v1","url_pdf":"http://arxiv.org/pdf/1805.00270v1.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":"capturing-ambiguity-in-crowdsourcing-frame","repo_url":"https://github.com/CrowdTruth/FrameDisambiguation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}