面向法律裁判文书的法条推荐方法

Law Article Prediction Method for Legal Judgment Documents

近年来,司法领域中针对法律裁判文书的分析和基于案例事实描述的结果预测已成为计算法律学的热点研究问题。法条推荐任务是基于司法案例的事实描述预测该案例适用的法条,已成为智慧司法的一项重要研究内容。通过分析法律文书的事实描述和法条的具体司法解释,挖掘司法文书事实描述部分的特征,提出了基于多模型融合的法条推荐方法。基于“中国法研杯”司法人工智能挑战赛中的公开数据,构建了3个不同规模的实验数据集,并分别在不同数据集上进行了多组实验。实验结果表明,相比于单一的法条推荐模型,所提方法能有效地提高任务的准确率,并且能较好地解决单一案例事实描述对应多个法条的推荐问题。

Inrecent years,the analysis of legal judgment documents and the prediction of results based on case facts in the judicial field have become the hot research topics in AI law.The law article prediction task is based on the factual description of the judicial case to predict the applicable law of the cases,which has become an important research content of wisdom justice.After analyzing the factual description of the legal documents and the specific judicial interpretation of the law,and excavating the characteristics of the factual description part of the judicial document,a method of recommending the law based on multi-model fusion was proposed.Based on the public dataset in the “CAIL2018” Judicial Artificial Intelligence Challenge,three datasets were constructed from different angles,and multiple sets of experiments were performed on each dataset.The experimental results show that the proposed method is simpler than the single model of law article prediction.The proposed method can effectively improve the accuracy of the task,and can better solve the recommendation problem of multiple cases in a single case fact description.