{"type":"ScholarlyArticle","title":"Relational Triple Extraction: One Step is Enough","authors":[{"name":"Yu-Ming Shang","slug":"yu-ming-shang","given":"Yu-Ming","family":"Shang","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology , Beijing , China"},{"name":"Heyan Huang","slug":"heyan-huang","given":"Heyan","family":"Huang","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology , Beijing , China"},{"name":"Xin Sun","slug":"xin-sun","given":"Xin","family":"Sun","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology , Beijing , China"},{"name":"Wei Wei","slug":"wei-wei","given":"Wei","family":"Wei","affiliation":"Huazhong University of Science and Technology , Hu'bei , China"},{"name":"Xian-Ling Mao","slug":"xian-ling-mao","given":"Xian-Ling","family":"Mao","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology , Beijing , China"}],"abstract":"Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction. Existing approaches usually contain two fundamental steps: (1) finding the boundary positions of head and tail entities; (2) concatenating specific tokens to form triples. However, nearly all previous methods suffer from the problem of error accumulation, i.e., the boundary recognition error of each entity in step (1) will be accumulated into the final combined triples. To solve the problem, in this paper, we introduce a fresh perspective to revisit the triple extraction task and propose a simple but effective model, named DirectRel.  Specifically, the proposed model first generates candidate entities through enumerating token sequences in a sentence, and then transforms the triple extraction task into a linking problem on a ``head -> tail\" bipartite graph. By doing so, all triples can be directly extracted in only one step. Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines.","doi":"10.24963/ijcai.2022/605","url":"https://ijcai.org/proceedings/2022/605","pdf":"https://ijcai.org/proceedings/2022/0605.pdf","markdown":"https://ijcai.org/proceedings/2022/605.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/605","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Natural Language Processing","track":"Main Track","number":605,"pages":{"first":4360,"last":4366},"keywords":["Natural Language Processing: Information Extraction","Natural Language Processing: Knowledge Extraction"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986960","length_sec":67,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985103","length_sec":697,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chan and Roth, 2011] Yee Seng Chan and Dan Roth. 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