{"type":"ScholarlyArticle","title":"A Relation-Specific Attention Network for Joint Entity and Relation Extraction","authors":[{"name":"Yue Yuan","slug":"yue-yuan","given":"Yue","family":"Yuan","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; University of Chinese Academy of Sciences , School of Cyber Security"},{"name":"Xiaofei Zhou","slug":"xiaofei-zhou","given":"Xiaofei","family":"Zhou","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; University of Chinese Academy of Sciences , School of Cyber Security"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"Faculty of Information Technology , Monash University"},{"name":"Qiannan Zhu","slug":"qiannan-zhu","given":"Qiannan","family":"Zhu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; University of Chinese Academy of Sciences , School of Cyber Security"},{"name":"Zeliang Song","slug":"zeliang-song","given":"Zeliang","family":"Song","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; University of Chinese Academy of Sciences , School of Cyber Security"},{"name":"Li Guo","slug":"li-guo","given":"Li","family":"Guo","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; University of Chinese Academy of Sciences , School of Cyber Security"}],"abstract":"Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.","doi":"10.24963/ijcai.2020/561","url":"https://ijcai.org/proceedings/2020/561","pdf":"https://ijcai.org/proceedings/2020/0561.pdf","markdown":"https://ijcai.org/proceedings/2020/561.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/561","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":561,"pages":{"first":4054,"last":4060},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Natural Language Processing: Information Extraction","Natural Language Processing: Knowledge Extraction","Data Mining: Mining Text, Web, Social Media"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24683/file_24683.m3u8","length_sec":278,"provider":"hls"}],"references":[{"position":1,"raw":"[Chan and Roth, 2011] Yee Seng Chan and Dan Roth. 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