该文针对法律领域民事案件中的“交通事故”类案件进行研究,期望在该“交通事故”数据集上实现自动判案。从“中国裁判文书网”采集14 000条数据文本,并对数据进行人工标注。基于对数据集的分析,分别对数据进行粗粒度和细粒度分类,粗粒度为4类,细粒度为8类。该文使用了三种模型: 基于SVM的模型、基于BI-GRU的模型和基于Attention+BI-GRU的模型。实验结果表明: 在该数据集上,对数据进行粗粒度分类时,基于Attention+BI-GRU的模型F1值为80.26%,基于SVM的模型为77.24%,基于BI-GRU的模型为72.65%。在细粒度分类时,基于BI-GRU的模型F1值为48.59%,基于SVM的模型为38.29%,基于Attention+BI-GRU的模型为40.87%。
Abstract
This article investigates the automatic judgment on the “traffic accidents” in civil cases of the legal field. The 14 000 samples are collected from the “China Jadgment Document Network.” Three models are examined, i.e. SVM-based model, BI-GRU-based model, and Attention+BI-GRU-based model, to classify the cases from the “China Judgment Document Network” into four-class and eight-class, respectively. The experimental results show that: the Attention+BI-GRU top-ranked with 80.26% F1 in the first task, while the BI-GRU model 48.59% F1 in the latter.
关键词
自动判案 /
神经网络 /
支持向量机 /
交通事故
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Key words
automatic judgment /
neural network /
SVM /
traffic accident
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参考文献
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脚注
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基金
国家社会科学基金(14BYY096);河南省科技厅科技攻关项目(172102210478)
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