{"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/qa4ie-a-question-answering-based-framework","title":"QA4IE: A Question Answering based Framework for Information Extraction","arxiv_id":"1804.03396","date":"2018-04-10","proceeding":null,"authors":["Lin Qiu","Hao Zhou","Yanru Qu","Wei-Nan Zhang","Suoheng Li","Shu Rong","Dongyu Ru","Lihua Qian","Kewei Tu","Yong Yu"],"abstract":"Information Extraction (IE) refers to automatically extracting structured\nrelation tuples from unstructured texts. Common IE solutions, including\nRelation Extraction (RE) and open IE systems, can hardly handle cross-sentence\ntuples, and are severely restricted by limited relation types as well as\ninformal relation specifications (e.g., free-text based relation tuples). In\norder to overcome these weaknesses, we propose a novel IE framework named\nQA4IE, which leverages the flexible question answering (QA) approaches to\nproduce high quality relation triples across sentences. Based on the framework,\nwe develop a large IE benchmark with high quality human evaluation. This\nbenchmark contains 293K documents, 2M golden relation triples, and 636 relation\ntypes. We compare our system with some IE baselines on our benchmark and the\nresults show that our system achieves great improvements.","url_abs":"http://arxiv.org/abs/1804.03396v2","url_pdf":"http://arxiv.org/pdf/1804.03396v2.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":"qa4ie-a-question-answering-based-framework","repo_url":"https://github.com/SJTU-lqiu/QA4IE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03396","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}