{"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/joint-extraction-of-entities-and-relations","title":"Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme","arxiv_id":"1706.05075","date":"2017-06-07","proceeding":"ACL 2017 7","authors":["Suncong Zheng","Feng Wang","Hongyun Bao","Yuexing Hao","Peng Zhou","Bo Xu"],"abstract":"Joint extraction of entities and relations is an important task in\ninformation extraction. To tackle this problem, we firstly propose a novel\ntagging scheme that can convert the joint extraction task to a tagging problem.\nThen, based on our tagging scheme, we study different end-to-end models to\nextract entities and their relations directly, without identifying entities and\nrelations separately. We conduct experiments on a public dataset produced by\ndistant supervision method and the experimental results show that the tagging\nbased methods are better than most of the existing pipelined and joint learning\nmethods. What's more, the end-to-end model proposed in this paper, achieves the\nbest results on the public dataset.","url_abs":"http://arxiv.org/abs/1706.05075v1","url_pdf":"http://arxiv.org/pdf/1706.05075v1.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":"joint-extraction-of-entities-and-relations","repo_url":"https://github.com/kyzhouhzau/CCLNER","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"joint-extraction-of-entities-and-relations","repo_url":"https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-nyt-single","task":"Relation Extraction","dataset":"NYT-single","model":"NovelTagging","rank_in_archive_order":3,"of":3,"metrics":{"F1":"49.5"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-nyt11-hrl","task":"Relation Extraction","dataset":"NYT11-HRL","model":"NovelTagging","rank_in_archive_order":10,"of":12,"metrics":{"F1":"47.9"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-webnlg","task":"Relation Extraction","dataset":"WebNLG","model":"NovelTagging","rank_in_archive_order":14,"of":14,"metrics":{"F1":"28.3"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.05075","atlas_url":"https://app.syntology.ai/?focus=1706.05075","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}