Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high ac...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 109315 - 109324
Main Authors Zhu, Kongfan, Ma, Baosen, Huang, Tianhuan, Li, Zeqiang, Ma, Haoyang, Li, Yujun
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Algorithms
Classification
Correlation
Crime
Feature extraction
hierarchical attention
Law
logical correlation
Multi-charge prediction
Predictive models
sequence generation
Structural hierarchy
Task analysis
Transforms
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Summary:The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high accuracy for the single-charge prediction, but their accuracy for the multi-charge prediction is low. To solve this problem, in this paper we introduce a novel hierarchical nested attention structure model with relevant law article information to predict the multi-charge classification of legal judgment documents. By considering the correlation between different charges, the accuracy of multi-charge prediction is greatly improved. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements over other state-of-the-art baselines.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2998486