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A Judicial Sentencing Method Based on Fused Deep Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11730))

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Abstract

Nowadays, the judicial system has been hard to satisfy the growing judicial needs of the people. Therefore, the introduction of artificial intelligence into the judicial field is an inevitable trend. This paper incorporates deep learning into intelligent judicial sentencing and proposes a comprehensive network fusion model based on massive legal documents. The proposed method combines multiple networks, e.g., recurrent neural network and convolutional neural network, in the procedure of sentencing prediction. Specially, we use text classification and post-classification regression to predict the defendant’s conviction, articles of law related to the case and prison term. Moreover, we use the simulated gradient descent method to build a fusion model. Experimental results on legal documents datasets justify the effectiveness of the proposed method in sentencing prediction. The fused network model outperforms each individual model in terms of higher accuracy and stability when predicting the conviction, law article and prison term.

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Acknowledgment

This paper is supported by the National Key Research and Development Program of China (Grant No. 2018YFC1801605), the Fund of State Key Laboratory for Novel Software Technology at Nanjing University (No. ZZKT2018B01).

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Correspondence to Yuhan Yin .

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Yin, Y., Yang, H., Zhao, Z., Chen, S. (2019). A Judicial Sentencing Method Based on Fused Deep Neural Networks. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-30490-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30489-8

  • Online ISBN: 978-3-030-30490-4

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