{"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/a-semantically-enriched-dataset-based-on","title":"A Semantically Enriched Dataset based on Biomedical NER for the COVID19 Open Research Dataset Challenge","arxiv_id":"2005.08823","date":"2020-05-18","proceeding":null,"authors":["Hermann Kroll","Jan Pirklbauer","Johannes Ruthmann","Wolf-Tilo Balke"],"abstract":"Research into COVID-19 is a big challenge and highly relevant at the moment. New tools are required to assist medical experts in their research with relevant and valuable information. The COVID-19 Open Research Dataset Challenge (CORD-19) is a \"call to action\" for computer scientists to develop these innovative tools. Many of these applications are empowered by entity information, i. e. knowing which entities are used within a sentence. For this paper, we have developed a pipeline upon the latest Named Entity Recognition tools for Chemicals, Diseases, Genes and Species. We apply our pipeline to the COVID-19 research challenge and share the resulting entity mentions with the community.","url_abs":"https://arxiv.org/abs/2005.08823v1","url_pdf":"https://arxiv.org/pdf/2005.08823v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-semantically-enriched-dataset-based-on","repo_url":"https://github.com/HermannKroll/CORD19BiomedicalNERDataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}