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Recent methods achieve considerable performance by mining semantic associations within the input sentence but they hardly exploit the equally important semantic meaning of relations. Most methods simply represent relations as numeric labels and the rich semantic information is not fully utilized to enhance performance. To address the issue, we decompose the task into two sequential subtasks, entity pairing and relation matching, from a novel perspective and then propose a semantic-preserving model SPRel. Specifically, SPRel first extracts entity pairs associated with at least one relation, and then matches them with certain relations according to the descriptive text of relations. Comprehensive experiments on two widely used datasets demonstrate that SPRel outperforms previous methods, particularly in handling complex scenarios.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae115","type":"journal-article","created":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T07:43:16Z","timestamp":1738482196000},"page":"346-354","source":"Crossref","is-referenced-by-count":1,"title":["Relation as text: a semantic-preserving method for relational triple extraction"],"prefix":"10.1093","volume":"68","author":[{"given":"Xinyi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology , No. 109 Deya Road, Changsha 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Strategic Assessments and Consultation Institute , Academy of Military Sciences, No. 1 Xianghongqi Road, Beijing 100000 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