【摘要】 案件罪名认定是司法业务的重要环节,尚缺乏有效的智能辅助工具和手段.针对案件定罪的难点问题,提出一种结合张量分解和双向LSTM(Long Short-Term Memory)神经网络的案件定罪方法.该方法将案件数据表示为张量,并在张量分解过程中引入模板张量.模板张量可以在双向LSTM神经网络分类模型的训练过程不断的被优化,使得分解后的核心张量包含更加有效的张量结构和特征信息,有助于提高后续分类模型的准确性,实现案件罪名的精准认定.实验结果表明:所提出的基于张量分解和双向LSTM的司法案件定罪方法比现有方法具有更好的准确性.更多还原

【Abstract】 Conviction in judicial cases is an important part of judicial business, but there is still a lack of effective intelligent auxiliary tools and methods.Aiming at the difficult problem of conviction in judicial cases, a method combining tensor decomposition and Bi-LSTM neural network is proposed.This method represents the case data as a tensor and introduces a template tensor in the tensor decomposition process.The template tensor can be continuously optimized during the training process of Bi-LSTM neural network classification model, so that the decomposed core tensor contains more effective tensor structure and feature information, which is helpful to improve the accuracy of the subsequent classification model and realize the accurate conviction in judicial cases.The experimental results show that the proposed method for conviction in judicial cases based on tensor decomposition and Bi-LSTM has better accuracy than the existing methods.更多还原