{"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/cross-type-biomedical-named-entity","title":"Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning","arxiv_id":"1801.09851","date":"2018-01-30","proceeding":null,"authors":["Xuan Wang","Yu Zhang","Xiang Ren","Yuhao Zhang","Marinka Zitnik","Jingbo Shang","Curtis Langlotz","Jiawei Han"],"abstract":"Motivation: State-of-the-art biomedical named entity recognition (BioNER)\nsystems often require handcrafted features specific to each entity type, such\nas genes, chemicals and diseases. Although recent studies explored using neural\nnetwork models for BioNER to free experts from manual feature engineering, the\nperformance remains limited by the available training data for each entity\ntype. Results: We propose a multi-task learning framework for BioNER to\ncollectively use the training data of different types of entities and improve\nthe performance on each of them. In experiments on 15 benchmark BioNER\ndatasets, our multi-task model achieves substantially better performance\ncompared with state-of-the-art BioNER systems and baseline neural sequence\nlabeling models. Further analysis shows that the large performance gains come\nfrom sharing character- and word-level information among relevant biomedical\nentities across differently labeled corpora.","url_abs":"http://arxiv.org/abs/1801.09851v4","url_pdf":"http://arxiv.org/pdf/1801.09851v4.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":"cross-type-biomedical-named-entity","repo_url":"https://github.com/yuzhimanhua/lm-lstm-crf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"cross-type-biomedical-named-entity","repo_url":"https://github.com/yuzhimanhua/Multi-BioNER","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"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":"type","task_name":"Vocal Bursts Type Prediction"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.09851","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}