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Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions

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A Correction to this article was published on 19 November 2019

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Abstract

This paper aims to provide a cognitive computing framework to meet the challenges of semantic understanding, knowledge learning and judicial reasoning in the Chinese legal domain. In our framework, legal factors are first represented in a formal way; secondly, legal factors are extracted, and concepts and their relations are augmented with a combination of rule-based and deep learning methods; thirdly, a predication model is generated and trained to make judicial decisions. When a fact description is brought into the model, the probability of judicial decisions will be given automatically. Two elementary results are obtained: I. Our method can effectively predict the decisions for divorce cases with different expression styles, and offers better performance than traditional methods like Support Vector Machine (SVM); II. Our machine learning predicting results can be easily understood by general public as applied induction rules are given.

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  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Acknowledgements

This work is supported by national high technology research and development plans (863 plan) (No.2013AA064303).

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Authors and Affiliations

  1. School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China

    Jiajing Li & Guoying Zhang

  2. Nanjing Wangganzhicha Information Tech. Inc., Nanjing, 210094, China

    Jiajing Li, Longxue Yu & Tao Meng

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  1. Jiajing Li
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  2. Guoying Zhang
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  3. Longxue Yu
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  4. Tao Meng
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Correspondence to Jiajing Li.

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Li, J., Zhang, G., Yu, L. et al. Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions. J Sign Process Syst 91, 1159–1167 (2019). https://doi.org/10.1007/s11265-018-1429-9

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