{"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/multi-task-identification-of-entities","title":"Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction","arxiv_id":"1808.09602","date":"2018-08-29","proceeding":"EMNLP 2018 10","authors":["Yi Luan","Luheng He","Mari Ostendorf","Hannaneh Hajishirzi"],"abstract":"We introduce a multi-task setup of identifying and classifying entities,\nrelations, and coreference clusters in scientific articles. We create SciERC, a\ndataset that includes annotations for all three tasks and develop a unified\nframework called Scientific Information Extractor (SciIE) for with shared span\nrepresentations. The multi-task setup reduces cascading errors between tasks\nand leverages cross-sentence relations through coreference links. Experiments\nshow that our multi-task model outperforms previous models in scientific\ninformation extraction without using any domain-specific features. We further\nshow that the framework supports construction of a scientific knowledge graph,\nwhich we use to analyze information in scientific literature.","url_abs":"http://arxiv.org/abs/1808.09602v1","url_pdf":"http://arxiv.org/pdf/1808.09602v1.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":"multi-task-identification-of-entities","repo_url":"https://bitbucket.org/luanyi/scierc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"multi-task-identification-of-entities","repo_url":"https://github.com/KeLi-gavin/CS8750","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"multi-task-identification-of-entities","repo_url":"https://github.com/YerevaNN/SciERC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"multi-task-identification-of-entities","repo_url":"https://github.com/danilo-dessi/skg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"multi-task-identification-of-entities","repo_url":"https://github.com/luanyi/DyGIE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"coreference-resolution","task_name":"Coreference Resolution"},{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[{"slug":"scierc","name":"SciERC","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on","task":"Joint Entity and Relation Extraction","dataset":"SciERC","model":"SciIE","rank_in_archive_order":8,"of":11,"metrics":{"Cross Sentence":"No","Entity F1":"64.20","Relation F1":"39.30"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-scierc","task":"Named Entity Recognition (NER)","dataset":"SciERC","model":"SCIIE","rank_in_archive_order":7,"of":7,"metrics":{"F1":"64.20"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.09602","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}