大数据 (Mar 2022)
Research on auxiliary division method based on convolutional neural network
Abstract
The court system mainly has two modes: manual designated division and simple random division.The above method cannot achieve automatic matching of persons and cases, and there are drawbacks such as money cases and relationship cases.At present, the research on division method mainly has two difficulties: judge’s representation and case matching.Combining the judge’s historical trial data, the judge’s expertise in the judge’s representation was integrated, and a judge representation method that integrates the quality of the trial was proposed.Then, the abstract semantic feature vectors of different granularities in the case representation and the judge representation were learned through the convolutional neural network, the cosine similarity between the case and the feature vectors of multiple judges was calculated, and vector similarity was used to indicate the matching degree between the case and the judge, the top N judges with high matching value were output as recommended judges for the case.Experiments with real data from a court in Guizhou Province, and the results show that the accuracy of the method for recommending judges is 80% higher than the traditional method.
