A Grey BP Neural Network-Based Model for Prediction of Court Decision Service Rate

Zhao, Gang; Shi, Hongyi; Wang, Jifa

doi:10.1155/2022/7364375

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Work [ 24 ] proposed a process supervision-based model for predicting legal decisions. Work [ 25 ] proposed an evaluation model of court judgment system based on grey system theory and BP neural network algorithm. Work [ 26 ] proposed a decision assistance method using restricted tensor factorization and relation-driven recurrent neural networks.…”

Section: Related Workmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

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Section: Related Workmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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“…Work [ 21 ] used CNN for text classification and achieved better results than other models. Work [ 22 ] designed a court judgment evaluation model. The evaluation model is based on BP neural network.…”

Section: Related Workmentioning

confidence: 99%

LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Smart court technologies are making full use of modern science to promote the modernization of the trial system and trial capabilities, for example, artificial intelligence, Internet of things, and cloud computing. The smart court technologies can improve the efficiency of case handling and achieving convenience for the people. Article recommendation is an important part of intelligent trial. For ordinary people without legal background, the traditional information retrieval system that searches laws and regulations based on keywords is not applicable because they do not have the ability to extract professional legal vocabulary from complex case processes. This paper proposes a law recommendation framework, called LawRec, based on Bidirectional Encoder Representation from Transformers (BERT) and Skip-Recurrent Neural Network (Skip-RNN) models. It intends to integrate the knowledge of legal provisions with the case description and uses the BERT model to learn the case description text and legal knowledge, respectively. At last, laws and regulations for cases can be recommended. Experiment results show that the proposed LawRec can achieve better performance than state-of-the-art methods.

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A Grey BP Neural Network-Based Model for Prediction of Court Decision Service Rate

Cited by 2 publications

References 6 publications

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis

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