{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T09:48:34Z","timestamp":1772531314540,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/561","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"4054-4060","source":"Crossref","is-referenced-by-count":103,"title":["A Relation-Specific Attention Network for Joint Entity and Relation Extraction"],"prefix":"10.24963","author":[{"given":"Yue","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &University of Chinese Academy of Sciences, School of Cyber Security"}]},{"given":"Xiaofei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &University of Chinese Academy of Sciences, School of Cyber Security"}]},{"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University"}]},{"given":"Qiannan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &University of Chinese Academy of Sciences, School of Cyber Security"}]},{"given":"Zeliang","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, School of Cyber Security"}]},{"given":"Li","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &University of Chinese Academy of Sciences, School of Cyber Security"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:15:58Z","timestamp":1594246558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/561"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/561","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}