{"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/single-versus-multiple-annotation-for-named","title":"Single versus Multiple Annotation for Named Entity Recognition of Mutations","arxiv_id":"2101.07450","date":"2021-01-19","proceeding":null,"authors":["David Martinez Iraola","Antonio Jimeno Yepes"],"abstract":"The focus of this paper is to address the knowledge acquisition bottleneck for Named Entity Recognition (NER) of mutations, by analysing different approaches to build manually-annotated data. We address first the impact of using a single annotator vs two annotators, in order to measure whether multiple annotators are required. Once we evaluate the performance loss when using a single annotator, we apply different methods to sample the training data for second annotation, aiming at improving the quality of the dataset without requiring a full pass. We use held-out double-annotated data to build two scenarios with different types of rankings: similarity-based and confidence based. We evaluate both approaches on: (i) their ability to identify training instances that are erroneous (cases where single-annotator labels differ from double-annotation after discussion), and (ii) on Mutation NER performance for state-of-the-art classifiers after integrating the fixes at different thresholds.","url_abs":"https://arxiv.org/abs/2101.07450v1","url_pdf":"https://arxiv.org/pdf/2101.07450v1.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":"single-versus-multiple-annotation-for-named","repo_url":"https://github.com/rishabgit/genomic-info-from-papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}