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Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning the local connections between relations. However, they are essentially limited because of failing to capture the global topology structure of relation ties and may easily fall into a locally optimal solution. To address this issue, we propose a novel force-directed graph to comprehensively learn relation ties. Specifically, we first construct a graph according to the global co-occurrence of all relations. Then, we borrow the idea of Coulomb\u2019s law from physics and introduce the concept of attractive force and repulsive force into this graph to learn correlation and mutual exclusion between relations. Finally, the obtained relation representations are applied as an inter-dependent relation classifier. Extensive experimental results demonstrate that our method is capable of modeling global correlation and mutual exclusion between relations, and outperforms the state-of-the-art baselines. In addition, the proposed force-directed graph can be used as a module to augment existing relation extraction systems and improve their performance.<\/jats:p>","DOI":"10.1145\/3520082","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T13:37:22Z","timestamp":1646833042000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction"],"prefix":"10.1145","volume":"41","author":[{"given":"Yu-Ming","family":"Shang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology and Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China"}]},{"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology and Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China"}]},{"given":"Xin","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology and Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"given":"Xian-Ling","family":"Mao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1388","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Alt Christoph","year":"2019","unstructured":"Christoph Alt, Marc H\u00fcbner, and Leonhard Hennig. 2019. 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