{"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/automatic-extraction-of-commonsense","title":"Automatic Extraction of Commonsense LocatedNear Knowledge","arxiv_id":"1711.04204","date":"2017-11-11","proceeding":"ACL 2018 7","authors":["Frank F. Xu","Bill Yuchen Lin","Kenny Q. Zhu"],"abstract":"LocatedNear relation is a kind of commonsense knowledge describing two\nphysical objects that are typically found near each other in real life. In this\npaper, we study how to automatically extract such relationship through a\nsentence-level relation classifier and aggregating the scores of entity pairs\nfrom a large corpus. Also, we release two benchmark datasets for evaluation and\nfuture research.","url_abs":"http://arxiv.org/abs/1711.04204v3","url_pdf":"http://arxiv.org/pdf/1711.04204v3.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":"automatic-extraction-of-commonsense","repo_url":"https://github.com/adapt-sjtu/commonsense-locatednear","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}