{"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/fastcontext-an-efficient-and-scalable","title":"FastContext: an efficient and scalable implementation of the ConText algorithm","arxiv_id":"1905.00079","date":"2019-04-30","proceeding":null,"authors":["Jianlin Shi","John F. Hurdle"],"abstract":"Objective: To develop and evaluate FastContext, an efficient, scalable\nimplementation of the ConText algorithm suitable for very large-scale clinical\nnatural language processing. Background: The ConText algorithm performs with\nstate-of-art accuracy in detecting the experiencer, negation status, and\ntemporality of concept mentions in clinical narratives. However, the speed\nlimitation of its current implementations hinders its use in big data\nprocessing. Methods: We developed FastContext through hashing the ConText's\nrules, then compared its speed and accuracy with JavaConText and\nGeneralConText, two widely used Java implementations. Results: FastContext ran\ntwo orders of magnitude faster and was less decelerated by rule increase than\nthe other two implementations used in this study for comparison. Additionally,\nFastContext consistently gained accuracy improvement as the rules increased\n(the desired outcome of adding new rules), while the other two implementations\ndid not. Conclusions: FastContext is an efficient, scalable implementation of\nthe popular ConText algorithm, suitable for natural language applications on\nvery large clinical corpora.","url_abs":"http://arxiv.org/abs/1905.00079v1","url_pdf":"http://arxiv.org/pdf/1905.00079v1.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":"fastcontext-an-efficient-and-scalable","repo_url":"https://github.com/jianlins/FastContext","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"negation","task_name":"Negation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}