计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 337-342.doi: 10.11896/jsjkx.201100212
曲浩1, 崔超然2, 王萧萧2, 苏雅茜2, 韩晓晖3, 尹义龙1
QU Hao1, CUI Chao-ran2, WANG Xiao-xiao2, SU Ya-xi2, HAN Xiao-hui3, YIN Yi-long1
摘要： 案件案由是对案件所涉及法律关系性质的描述,科学、完善的案由设置有利于正确适用法律,是人民法院实行案件分类管理的重要途径。案件案由预测技术指基于案件案情的文本描述由计算机自动给出案件所属类别。在案件属性预测研究中,由于低频类别的样本数量较少且难以学习相关特征,因此已有方法在数据处理部分通常会对低频类别样本进行剔除。然而,在案件案由预测问题中,关键的挑战正是如何对属于低频案由的案件做出准确预测。为此,文中提出了一种基于非均衡数据层次学习的案件案由预测方法。在案件案由预测中,根据案由层次结构将案由划分为一级案由和二级案由,二级案由中的大量尾部类别被汇聚成上层样本数较多的大类,进而通过层次学习的方式来实现二级案由的预测,使二级案由有一级案由的信息支撑。最后,引入调整数据不平衡的损失函数来实现案件案由的预测。实验结果表明,所提方法整体优于对比方法,其平均精确率比现有方法提高了4.81%,这表明通过层次学习和引入非均衡数据损失函数能较好地解决案件案由预测问题。
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