{"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/relation-classification-with-entity-type","title":"Relation Classification with Entity Type Restriction","arxiv_id":"2105.08393","date":"2021-05-18","proceeding":"Findings (ACL) 2021 8","authors":["Shengfei Lyu","Huanhuan Chen"],"abstract":"Relation classification aims to predict a relation between two entities in a sentence. The existing methods regard all relations as the candidate relations for the two entities in a sentence. These methods neglect the restrictions on candidate relations by entity types, which leads to some inappropriate relations being candidate relations. In this paper, we propose a novel paradigm, RElation Classification with ENtity Type restriction (RECENT), which exploits entity types to restrict candidate relations. Specially, the mutual restrictions of relations and entity types are formalized and introduced into relation classification. Besides, the proposed paradigm, RECENT, is model-agnostic. Based on two representative models GCN and SpanBERT respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT. Experimental results on a standard dataset indicate that RECENT improves the performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively. Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.","url_abs":"https://arxiv.org/abs/2105.08393v1","url_pdf":"https://arxiv.org/pdf/2105.08393v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-tacred","task":"Relation Extraction","dataset":"TACRED","model":"RECENT+SpanBERT","rank_in_archive_order":6,"of":40,"metrics":{"F1":"75.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.08393","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}