Tree-CP: an interpretable crime prediction model based on tree structure and keyword extraction

Qiya Chao; Shunyu Shao; Huaixiang Hu

doi:10.1117/12.3004911

9 October 2023 Tree-CP: an interpretable crime prediction model based on tree structure and keyword extraction

Qiya Chao, Shunyu Shao, Huaixiang Hu

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279114 (2023) https://doi.org/10.1117/12.3004911
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Legal judgement prediction (LJP) means to predict the judgement results according to the given facts of the cases. Charge Prediction (CP) as a sub-task for LJP, is a substantial research area in Judicial field. The key problems remain in CP task includes: (a). low-accuracy on low-frequency charges and similar charges; (b). the lack of interpretable model which are vital in judicial appliance. To address the aforementioned problems, we propose a CP model based on keyword extraction and tree structure, e. g. Tree-CP. By fusing the keywords into word embedded layer and deploying the concept tree in Attention layer, the interpretability of the predictions is solidly enhanced. Tree-CP leverages various data augmentation method on low frequency charges to amend the small sample pool. The features are extracted and fused via BiGRU and capsule network separately to improve the predicting accuracy. The comparative experiment proves the effectiveness of Tree-CP. And the ablation experiment shows the soundness of the model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiya Chao, Shunyu Shao, and Huaixiang Hu "Tree-CP: an interpretable crime prediction model based on tree structure and keyword extraction", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279114 (9 October 2023); https://doi.org/10.1117/12.3004911

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KEYWORDS

Feature extraction

Data modeling

Classification systems

Semantics

Statistical modeling

Ablation

Feature fusion

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