{"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/end-to-end-neural-relation-extraction-using","title":"End-to-end neural relation extraction using deep biaffine attention","arxiv_id":"1812.11275","date":"2018-12-29","proceeding":null,"authors":["Dat Quoc Nguyen","Karin Verspoor"],"abstract":"We propose a neural network model for joint extraction of named entities and\nrelations between them, without any hand-crafted features. The key contribution\nof our model is to extend a BiLSTM-CRF-based entity recognition model with a\ndeep biaffine attention layer to model second-order interactions between latent\nfeatures for relation classification, specifically attending to the role of an\nentity in a directional relationship. On the benchmark \"relation and entity\nrecognition\" dataset CoNLL04, experimental results show that our model\noutperforms previous models, producing new state-of-the-art performances.","url_abs":"http://arxiv.org/abs/1812.11275v1","url_pdf":"http://arxiv.org/pdf/1812.11275v1.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":"end-to-end-neural-relation-extraction-using","repo_url":"https://github.com/datquocnguyen/jointRE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-conll04","task":"Relation Extraction","dataset":"CoNLL04","model":"Biaffine attention","rank_in_archive_order":5,"of":16,"metrics":{"NER Macro F1":"86.2","RE+ Macro F1 ":"64.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.11275","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}