{"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/c-norm-a-neural-approach-to-few-shot-entity","title":"C-Norm: a neural approach to few-shot entity normalization","arxiv_id":null,"date":"2020-12-29","proceeding":"BMC Bioinformatics 2020 12","authors":["Arnaud Ferré","Louise Deléger","Robert Bossy","Pierre Zweigenbaum","Claire Nédellec"],"abstract":"Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics.","url_abs":"https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03886-8","url_pdf":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03886-8.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":"c-norm-a-neural-approach-to-few-shot-entity","repo_url":"https://github.com/ArnaudFerre/C-Norm","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"medical-concept-normalization","task_name":"Medical Concept Normalization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}