Harbin Institute of Technology, Harbin, China.
Comput Intell Neurosci. 2019 Jul 7;2019:6705405. doi: 10.1155/2019/6705405. eCollection 2019.
The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusations involved in legal cases is an urgent problem to be solved in legal judgment. The key to solving these problems lies in two points, namely, (1) characterization of legal cases and (2) classification and prediction of legal case data. Traditional methods of entity characterization rely on feature extraction, which is often based on vocabulary and syntax information. Thus, traditional entity characterization often requires extensive energy and has poor generality, thus introducing a large amount of computation and limitation to subsequent classification algorithms. This study proposes an intelligent judgment approach called RnRTD, which is based on the relationship-driven recurrent neural network (rdRNN) and restricted tensor decomposition (RTD). We represent legal cases as tensors and propose an innovative RTD method. RTD has low dependence on vocabulary and syntax and extracts the feature structure that is most favorable for improving the accuracy of the subsequent classification algorithm. RTD maps the tensors, which represent legal cases, into a specific feature space and transforms the original tensor into a core tensor and its corresponding factor matrices. This study uses rdRNN to continuously update and optimize the constraints in RTD so that rdRNN can have the best legal case classification effect in the target feature space generated by RTD. Simultaneously, rdRNN sets up a new gate and a similar case list to represent the interaction between legal cases. In comparison with traditional feature extraction methods, our proposed RTD method is less expensive and more universal in the characterization of legal cases. Moreover, rdRNN with an RTD layer has a better effect than the recurrent neural network (RNN) only on the classification and prediction of multiple accusations in legal cases. Experiments show that compared with previous approaches, our method achieves higher accuracy in the classification and prediction of multiple accusations in legal cases, and our algorithm is more interpretable.
利用智能判断技术辅助判断是当代社会法律案件判断发展的必然趋势。利用大数据和人工智能技术准确判断法律案件中涉及的多项指控是法律判断中亟待解决的问题。解决这些问题的关键在于两点，即（1）法律案件的特征化和（2）法律案件数据的分类和预测。传统的实体特征化方法依赖于特征提取，而特征提取通常基于词汇和语法信息。因此，传统的实体特征化方法往往需要大量的能量，并且通用性较差，从而对后续的分类算法引入了大量的计算和限制。本研究提出了一种称为 RnRTD 的智能判断方法，该方法基于关系驱动的递归神经网络（rdRNN）和受限张量分解（RTD）。我们将法律案件表示为张量，并提出了一种创新的 RTD 方法。RTD 对词汇和语法的依赖性较低，提取最有利于提高后续分类算法准确性的特征结构。RTD 将表示法律案件的张量映射到特定的特征空间中，并将原始张量转换为核心张量及其对应的因子矩阵。本研究使用 rdRNN 不断更新和优化 RTD 中的约束，以使 rdRNN 在 RTD 生成的目标特征空间中对法律案件具有最佳的分类效果。同时，rdRNN 设置了一个新的门和一个相似案例列表来表示法律案件之间的交互。与传统的特征提取方法相比，我们提出的 RTD 方法在法律案件的特征化方面更具成本效益和通用性。此外，在法律案件多项指控的分类和预测方面，带有 RTD 层的 rdRNN 比仅使用 RNN 的效果更好。实验表明，与以前的方法相比，我们的方法在法律案件多项指控的分类和预测方面具有更高的准确性，并且我们的算法更具可解释性。
