Research on case classification algorithm based on BERT-CNN

Abstract

At present, procuratorial organs are facing the problem of diverse and complex types of cases when they initiate public welfare lawsuit. The current research on case classification are not enough to meet requirements of procuratorial organs for future efficiency of public welfare lawsuit. For these problems, we propose case classification model based on BERT-CNN to solve them. First, for the ultra-long sequence input to the model, a dynamic programming algorithm is used for block processing, and then the MemRecall method of CogLTX is used to extract the text blocks that can complete the task and reassemble a new sequence as input. Secondly, BERT, as the word embedding layer, extracts features from the input large-scale source corpus to obtain rich semantic information and converts it into vector output. Finally, the CLS vector output by the BERT model is used as the input of the CNN, which is convolved and pooled, and then spliced into a fully connected layer, and the case is classified through softmax. This thesis fine-tunes the BERT model to make it perform best on case classification. Experiments on the Legal AI Challenge dataset indicate the case classification algorithm based on BERT-CNN is an improvement over others. its F1-score reaches 95.32%, which improved 1.44 percentage points.