TenRR: An Approach Based on Innovative Tensor Decomposition and Optimized Ridge Regression for Judgment Prediction of Legal Cases

Abstract

With the development of big data and artificial intelligence technologies, the use of computers to assist judgments in legal cases has become a popular topic. Traditional methods for judgment prediction mainly depend on feature models and classification algorithms. However, feature models require considerable expert knowledge and manual annotation work. They have strong dependence on vocabulary and grammar information in datasets, which is unconducive to improving the universality and accuracy of subsequent prediction algorithms. Meanwhile, the outputs of classification algorithms are discrete prediction results with coarse granularities. This paper proposes a new algorithm based on innovative tensor decomposition and ridge regression for judgment prediction of legal cases, namely, TenRR. TenRR is mainly divided into three steps. First, we propose a tensor representation method, namely, RTenr. RTenr expresses legal cases as three-dimensional tensors. Second, we propose an innovative tensor decomposition algorithm, namely, ITend. ITend decomposes original tensors representing legal cases into core tensors. Lastly, we propose an optimized ridge algorithm, namely, ORidge, to construct a judgment prediction model for legal cases. We further propose an optimization algorithm for ORidge to ITend; thus, core tensors obtained using ITend carry tensor elements and tensor structure information that is most beneficial to improving the accuracy of ORidge. Core tensors greatly reduce the dimension of original tensors. They eliminate the meaningless, redundant, and inaccurate information in original tensors. Experiments show that our method has higher accuracy than traditional methods for judgment prediction.