›› 2018,Vol. 33 ›› Issue (4): 756-767.doi: 10.1007/s11390-018-1854-z

所属专题: Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Special Section on Computer Networks and Distributed Computing • 上一篇    下一篇

一种用于罚金判定的主题模型

Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo   

  1. Software Institute, Nanjing University, Nanjing 210093, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • 收稿日期:2017-12-27 修回日期:2018-05-08 出版日期:2018-07-05 发布日期:2018-07-05
  • 作者简介:Tie-Ke He is a research assistant at Software Institute, Nanjing University, Nanjing. He got his B.S., M.S. and Ph.D. degrees in software engineering from Nanjing University, Nanjing, in 2010, 2012 and 2017, respectively. His research interests include recommender systems and knowledge graph.
  • 基金资助:

    This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0800805 and the National Natural Science Foundation of China under Grant No. 61690201.

PTM: A Topic Model for the Inferring of the Penalty

Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo   

  1. Software Institute, Nanjing University, Nanjing 210093, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2017-12-27 Revised:2018-05-08 Online:2018-07-05 Published:2018-07-05
  • About author:Tie-Ke He is a research assistant at Software Institute, Nanjing University, Nanjing. He got his B.S., M.S. and Ph.D. degrees in software engineering from Nanjing University, Nanjing, in 2010, 2012 and 2017, respectively. His research interests include recommender systems and knowledge graph.
  • Supported by:

    This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0800805 and the National Natural Science Foundation of China under Grant No. 61690201.

决定罚金数额或对法律案件的处罚一直是一个复杂的过程,需要进行大量的协调和商定。尽管有基于规则和条件的司法研究,但是人工智能和机器学习仍然很少用于研究刑罚推理。本文的目的是将最先进的人工智能技术应用于该问题的解决范围。我们首先分析了14.5万个法律案例,并观察到有两种具有独特特征的标签:时间标签和空间标签。时间标签和空间标签在最终惩罚属于同一类别时趋于一致。在此基础上,我们提出了一种基于规则的概率生成模型,即惩罚主题模型(PTM),来推断案件的法律案例的主题以及案例判决中嵌入的时间和空间模式。然后,利用这些学习的知识自动地将所有的案例集合起来。我们进行了广泛的实验以评估PTM在实际的大规模数据集上的性能。实验结果表明了该方法的在适当使用下的优越性。

Abstract: Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model (PTM), to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.

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