{"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/t-rex-a-large-scale-alignment-of-natural","title":"T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples","arxiv_id":null,"date":"2018-05-01","proceeding":"LREC 2018 5","authors":["Hady Elsahar","Pavlos Vougiouklis","Arslen Remaci","Christophe Gravier","Jonathon Hare","Frederique Laforest","Elena Simperl"],"abstract":"","url_abs":"https://aclanthology.org/L18-1544","url_pdf":"https://aclanthology.org/L18-1544.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":"t-rex-a-large-scale-alignment-of-natural","repo_url":"https://github.com/hadyelsahar/RE-NLG-Dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"knowledge-base-population","task_name":"Knowledge Base Population"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[{"slug":"t-rex","name":"T-REx","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}