Similar case matching based on BERT and feature extraction
Similar legal case retrieval is a special retrieval task in which similar cases need to be searched from given candidate cases for a given query case.Unlike traditional text matching,legal case matching has the characteristics of long text and strong subjectivity.To address these issues in similar case matching in legal cases,this thesis proposes a similar case retrieval method based on case elements.This thesis first uses general corpora to fine-tune the BERT model,then encodes the context-specific semantic information of case documents using the paragraph aggregation method,and integrates legal document data into the model.Extensive experiments on the LeCaRD dataset were conducted in this paper,and the results show that the proposed model is superior to existing models.
万方数据
维普
