【摘要】 裁判文书数量的快速增长对自动化分类提出了迫切要求,然而已有研究缺乏在民事案件这一细分领域下以判决结果为分类标准的方法的研究,无法实现对民事案件判决结果的准确分类。文中将深度学习技术应用于民事案件判决结果分类领域,通过横向对比多种深度学习模型得出了该领域下表现较好的模型,并依据裁判文书的数据特点对该模型进行了进一步的优化。实验结果证明,Transformer模型的判决结果分类的宏精准率、宏召回率和宏F1分数均高于其他模型。通过对数据预处理流程的优化和对Transformer模型位置嵌入方式的优化,模型的性能指标提升了1%～2%。更多还原

【Abstract】 The rapid increase in the number of judgment documents puts forward an urgent need for automated classification.However, there is a lack of method in existing studies that use judgment results as the subject of classification in the subdivision of civil cases, and therefore they cannot achieve accurate classification of judgment results in civil cases.In this paper, we apply deep learning technology in the field of classification of judgment results of civil cases, and obtain a model with better perfor-mance in this field through horizontal comparison of multiple deep learning models.This model is further optimized based on the data characteristics of the judgment document.After experiments, the Transformer model’s macro precision rate, macro recall rate and macro F1 score in the judgment result classification are all higher than other models.By adjusting the data preprocessing process and adjusting the position embedding method of the Transformer model, the performance index of the model is increased by 1%～2%.更多还原