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Case Element Joint Extraction Based on Case Field Correlation and Dependency Graph Convolutional Network

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Abstract

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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Acknowledgements

The work was supported by National Natural Science Foundation of China (Grant Nos. 61972186, U21B2027, 61732005), National Key Research and Development Plan (Grant Nos. 2018YFC0830105, 2018YFC0830101, 2018YFC0830100), Yunnan provincial major science and technology special plan projects (Grant No. 202002AD080001-5, 202103AA080015), Yunnan high-tech industry development project (Grant No. 201606, 202105AC160018), Yunnan Basic Research Project (Grant No. 202001AS070014).

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Yu Zhang, Shengxiang Gao, Zhengtao Yu, Chengding Zhao, Peilian Zhao & Peifu Han

  2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Yu Zhang, Shengxiang Gao, Zhengtao Yu, Chengding Zhao, Peilian Zhao & Peifu Han

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  1. Yu Zhang
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Correspondence to Shengxiang Gao.

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Zhang, Y., Gao, S., Yu, Z. et al. Case Element Joint Extraction Based on Case Field Correlation and Dependency Graph Convolutional Network. Neural Process Lett 55, 5583–5595 (2023). https://doi.org/10.1007/s11063-022-11101-3

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