用问题库提升普通人精准查法律知识的能力
QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public
- 构建问题库生成训练样本,增强文档知识嵌入
- 相比传统方法检索更准更快且结果可解释
- 适合普通民众解决日常法律困扰
公众获取法律知识面临难题,因专业术语复杂且普通人缺乏基础理解。传统信息检索依赖用户精准提问,但实际中技术内容与用户认知存在巨大鸿沟。本文提出QBR方法,通过问题库(QB)作为桥梁,生成训练样本以增强文档内知识单元的嵌入表示,实现细粒度法律知识高效检索。实验表明,QBR在准确率、效率和可解释性上优于传统方法,显著提升检索结果的理解度,支持精准知识定位。案例研究显示,该方法已帮助公众解决日常生活中的法律问题,具备社会应用价值。
原文摘要 · Abstract (English)
Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.
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