智能计算机与应用2025,Vol.15Issue(1) :130-135.DOI:10.20169/j.issn.2095-2163.250120

基于BERT与要素提取的相似案例匹配

Similar case matching based on BERT and feature extraction

焦宇超 阎刚
智能计算机与应用2025,Vol.15Issue(1) :130-135.DOI:10.20169/j.issn.2095-2163.250120

基于BERT与要素提取的相似案例匹配

Similar case matching based on BERT and feature extraction

焦宇超 1阎刚1
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作者信息

  • 1. 河北工业大学 人工智能与数据科学学院,天津 300401
  • 折叠

摘要

相似法律案件检索是一项特殊的检索任务,对于给定的查询案例,需要从给定的候选案例中搜索相似的案例.与传统的文本匹配不同,法律案件匹配具有文本较长、主题性强的特点.针对上述问题,本文提出了一种基于案件要素的相似案例检索方法.首先对BERT模型使用通用语料进行微调;然后采用段落聚合方法,对案件文书上下文语义信息进行编码,同时将法律文书数据融入模型.本文在LeCaRD数据集上进行了广泛的实验,实验结果表明,本文提出的模型优于现有模型.

Abstract

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.

关键词

相似案例匹配/BERT/长文本/法律要素

Key words

similar case matching/BERT/long text/legal elements

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出版年

2025
智能计算机与应用
哈尔滨工业大学

智能计算机与应用

影响因子:0.357
ISSN:2095-2163
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