文献类型：会议论文

英文题名：Joint Entity-Relation Extraction Model for Epidemiological Reports Based on SMART Adversarial Training

作者：Sun, Jiazheng[1];Yan, Huaicheng[1];Li, Yue[1];Li, Hao[1];Bai, Ke[1];Li, Zhichen[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：14th Asian Control Conference (ASCC)

会议日期：JUL 05-08, 2024

会议地点：Dalian, PEOPLES R CHINA

语种：英文

外文关键词：entity-relation extraction; SMART adversarial training; CasRel network; epidemiological reports

摘要：Epidemiological investigation work requires the drafting of complex epidemiological reports, the information within which is crucial for tracing the origin of outbreaks and for preventive measures against future epidemics. Currently, each segment of text in these reports contains a significant number of triplets, most of which are overlapping. Traditional entity-relation extraction algorithms struggle to handle these overlapping triplets. In response to this situation, this paper introduces a joint entity-relation extraction model based on SMART adversarial training and the CasRel network. Through comparative experiments conducted on both the dataset constructed for this paper and publicly available datasets, the feasibility and effectiveness of the proposed model have been verified.