Charge prediction method combined with case elements sequence

Qian SUN; Yongbin QIN; Ruizhang HUANG; Lijuan LIU; Yanping CHEN

doi:10.11959/j.issn.2096-0271.2021058

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Charge prediction method combined with case elements sequence

TOPIC: BIG DATA-ENABLED INTELLIGENT APPLICATIONS | 更新时间：2024-06-03

- Charge prediction method combined with case elements sequence
- Big data research Vol. 7, Issue 6, Pages: 30-40(2021)
- 作者机构：
  
  1. 贵州大学计算机科学与技术学院，贵州贵阳 550025
  2. 公共大数据国家重点实验室，贵州贵阳 550025
  3. 贵州师范学院，贵州贵阳 550018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021058
  CLC： TP391.1
- Online First：2021-11，
  
  Published：15 November 2021
- 稿件说明：
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Qian SUN, Yongbin QIN, Ruizhang HUANG, et al. Charge prediction method combined with case elements sequence[J]. Big data research, 2021, 7(6): 30-40.
DOI：

Qian SUN, Yongbin QIN, Ruizhang HUANG, et al. Charge prediction method combined with case elements sequence[J]. Big data research, 2021, 7(6): 30-40. DOI： 10.11959/j.issn.2096-0271.2021058.

摘要

罪名预测指根据给定的案情事实找到适用罪名。现有罪名预测方法主要使用文本内容进行分类，但无法有效地利用文本中的案件要素。针对现有方法的不足，提出了一种结合案件要素序列的罪名预测方法。该方法将案情事实过程表示为一系列以“行为”为核心且具有时序关系的案件要素序列，然后利用图卷积神经网络进行表示，最后融合文本语义特征来预测案件罪名。实验表明，该方法比现有方法具有更好的预测性能。同时，该方法在对易混淆罪名的区分方面也有较好的表现。

Abstract

Charge prediction is to find the appropriate charges based on the facts of the given case.Existing methods mainly use text content for classification

but they cannot effectively use case elements.For the shortcomings of the existing methods

the method of accusation prediction based on the sequence of case elements was put forward.The way expressed the case factual processes as a series of case elements with “behavior” as the core and time-series relationship.Then graph convolutional network was used to represent.Finally

the semantic features of the text were fused to predict the crime.Experiments show that this method has better prediction performance than existing methods.Meanwhile

this method also has a good performance for the distinction between easily confusing charges.

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references

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