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论文作者：	Zhang Yu, Shengxiang Gao , Zhengtao Yu, Chengding Zhao, Peilian Zhao, Peifu Han.
论文名称：	Case Element Joint Extraction Based on Case Field Correlation and Dependency Graph Convolutional Network
论文发表刊物：	Neural Processing Letters
论文发表时间：	2023
卷号页码：
论文描述：
收录情况：	SCI Indexed
论文摘要：
The case element is a brief description of the case-related events. Extracting the case elements in the news text has great significance for downstream case field natural language processing tasks. In view of the case field relevance and intrinsic relevance of the case elements, this paper proposes a joint case element extraction method based on case domain correlation and graph convolutional network: modeling sentence contextual information by bi-directional long short-term memory networks, then using it to predict the case field correlation for guarantying the elements’ relevance of cases by joint learning; and modeling the dependency relationship of candidate elements by graph convolutional network to capture its intrinsic relevance. The experiments show that the method proposed in this paper improves accuracy rate by 6. 6% in extracting case elements.
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