Journal of Signal Processing Systems

, Volume 91, Issue 10, pp 1159–1167 | Cite as

Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions

  • Jiajing LiEmail author
  • Guoying Zhang
  • Longxue Yu
  • Tao Meng


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.


Artificial intelligence Cognitive computing Natural language processing Machine learning Legal science Judicial decisions 



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


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Electronic & Information EngineeringChina University of Mining and Technology (Beijing)BeijingChina
  2. 2.Nanjing Wangganzhicha Information Tech. Inc.NanjingChina

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