{"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-bootstrapping-machines-for-high","title":"Joint Bootstrapping Machines for High Confidence Relation Extraction","arxiv_id":"1805.00254","date":"2018-05-01","proceeding":"NAACL 2018 6","authors":["Pankaj Gupta","Benjamin Roth","Hinrich Schütze"],"abstract":"Semi-supervised bootstrapping techniques for relationship extraction from\ntext iteratively expand a set of initial seed instances. Due to the lack of\nlabeled data, a key challenge in bootstrapping is semantic drift: if a false\npositive instance is added during an iteration, then all following iterations\nare contaminated. We introduce BREX, a new bootstrapping method that protects\nagainst such contamination by highly effective confidence assessment. This is\nachieved by using entity and template seeds jointly (as opposed to just one as\nin previous work), by expanding entities and templates in parallel and in a\nmutually constraining fashion in each iteration and by introducing\nhigherquality similarity measures for templates. Experimental results show that\nBREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the\nart for four relationships.","url_abs":"http://arxiv.org/abs/1805.00254v1","url_pdf":"http://arxiv.org/pdf/1805.00254v1.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-bootstrapping-machines-for-high","repo_url":"https://github.com/pgcool/Joint-Bootstrapping-Machines","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"relationship-extraction-distant-supervised","task_name":"Relationship Extraction (Distant Supervised)"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}