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首页> 美国卫生研究院文献>Computational Intelligence and Neuroscience >RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases
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RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases

机译：RnRTD：基于关系驱动神经网络和受限制张量分解的智能方法用于法律案件中的多次控告判决

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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.
机译：在现代社会法律案件中，使用智能判断技术辅助判断是发展判断的必然趋势。利用大数据和人工智能技术准确确定诉讼案件中的多项指控是法律判决中亟待解决的问题。解决这些问题的关键在于两点，即（1）法律案件的表征和（2）法律案件数据的分类和预测。传统的实体表征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，传统的实体表征通常需要大量的精力并且通用性差，从而给后续的分类算法带来了大量的计算和限制。这项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，并提出一种创新的RTD方法。 RTD对词汇和语法的依赖性较低，并提取出最有利于提高后续分类算法准确性的特征结构。 RTD将代表法律案件的张量映射到特定的特征空间，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，以便rdRNN可以在RTD生成的目标特征空间中具有最佳的法律案例分类效果。同时，rdRNN设置了一个新的门和一个类似的案件列表，以表示法律案件之间的相互作用。与传统的特征提取方法相比，我们提出的RTD方法更便宜，并且在法律案件的特征描述方面更为通用。此外，具有RTD层的rdRNN仅在法律案件中对多种指控的分类和预测方面比循环神经网络（RNN）具有更好的效果。实验表明，与以前的方法相比，本方法在诉讼案件中多个指控的分类和预测中具有较高的准确性，并且算法更具解释性。

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