Judicial document intellectual processing using hybrid deep neural networks

WANG Wenguan; CHEN Yunwen; CAI Hua; ZENG Yanneng; YANG Huiyu

doi:10.16511/j.cnki.qhdxxb.2019.21.015

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Journal of Tsinghua University(Science and Technology) ›› 2019, Vol. 59 ›› Issue (7) : 505-511. DOI: 10.16511/j.cnki.qhdxxb.2019.21.015

SPECIAL SECTION: AI AND LAW

Judicial document intellectual processing using hybrid deep neural networks

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Abstract

This article presents a neural network model for crime prediction, legal article recommendation, and sentence prediction from judicial documents. The model is based on a hybrid attention and CNN model which combines the improved hierarchical attention network (iHAN) and the deep pyramid convolutional neural network (DPCNN) by ResNet. The F1-Scores (mean value of Micro-F1 and Macro-F1) for the crime prediction and related law samples from CAIL-2018 were 85% and 87%. The sentence prediction accuracy is impacted by differences in locations, dates, courts, judges, and defendant attitudes. The model adjusts well to these differences because of its high predictive ability and model generalization. The model prediction outputs for the recommended crime prediction and law items were then added to the model input for the sentence prediction task to further improve the model performance. The model got an excellent result in the sentence prediction task (CAIL-2018) with an F1-Score of over 77%.

Key words

judicial document processing / natural language understanding / verdict prediction / deep neural networks / attention model

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WANG Wenguan, CHEN Yunwen, CAI Hua, ZENG Yanneng, YANG Huiyu. Judicial document intellectual processing using hybrid deep neural networks[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(7): 505-511 https://doi.org/10.16511/j.cnki.qhdxxb.2019.21.015

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