{"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-neural-multi-task-learning-framework-to","title":"A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization","arxiv_id":"1812.06081","date":"2018-12-14","proceeding":null,"authors":["Sendong Zhao","Ting Liu","Sicheng Zhao","Fei Wang"],"abstract":"State-of-the-art studies have demonstrated the superiority of joint modelling\nover pipeline implementation for medical named entity recognition and\nnormalization due to the mutual benefits between the two processes. To exploit\nthese benefits in a more sophisticated way, we propose a novel deep neural\nmulti-task learning framework with explicit feedback strategies to jointly\nmodel recognition and normalization. On one hand, our method benefits from the\ngeneral representations of both tasks provided by multi-task learning. On the\nother hand, our method successfully converts hierarchical tasks into a parallel\nmulti-task setting while maintaining the mutual supports between tasks. Both of\nthese aspects improve the model performance. Experimental results demonstrate\nthat our method performs significantly better than state-of-the-art approaches\non two publicly available medical literature datasets.","url_abs":"http://arxiv.org/abs/1812.06081v1","url_pdf":"http://arxiv.org/pdf/1812.06081v1.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":"a-neural-multi-task-learning-framework-to","repo_url":"https://github.com/SendongZhao/Multi-Task-Learning-for-MER-and-MEN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"medical-named-entity-recognition","task_name":"Medical Named Entity Recognition"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"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":"https://app.syntology.ai/?focus=1812.06081","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}