融合分段编码与仿射机制的相似案例匹配方法

doi:10.6040/j.issn.1671-9352.1.2021.048

《山东大学学报(理学版)》 ›› 2023, Vol. 58 ›› Issue (1): 40-47.doi: 10.6040/j.issn.1671-9352.1.2021.048

• • 上一篇

融合分段编码与仿射机制的相似案例匹配方法

赖华^1,2,张恒滔^1,2,线岩团^1,2*,黄于欣^1,2

1.昆明理工大学信息工程与自动化学院, 云南昆明 650500;2.昆明理工大学云南省人工智能重点实验室, 云南昆明 650500

发布日期:2023-02-12
作者简介:赖华(1966— ),男,硕士,副教授,研究方向为智能信息处理、复杂过程建模与控制.E-mail:405904235@qq.com*通信作者简介:线岩团(1981— ),男,博士研究生,副教授,研究方向为自然语言处理、信息抽取、机器翻译.E-mail:xianyantuan@qq.com
基金资助:
国家自然科学基金资助项目(61966020);国家重点研发计划资助项目(2018YFC0830104,2018YFC0830105,2018YFC0830100);云南省基础研究计划资助项目(202001AT070046)

A similarity case matching method combining segment encoding and affine-mechanism

LAI Hua^1,2, ZHANG Heng-tao^1,2, XIAN Yan-tuan^1,2*, HUANG Yu-xin^1,2

1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

Published:2023-02-12

摘要/Abstract

摘要： 相似案例匹配任务旨在判断2篇裁判文书所描述的案件是否相似,通常被看作裁判文书的文本匹配问题,在司法审判过程中具有重要的应用。现有深度学习模型大多将案例长文本编码为单一向量表示,模型很难从长文本中学习到裁判文书之间的细微差异。考虑到案例文本各部分的内容较为固定,本文提出将案例长文本拆分为多个片断并分别编码,以便获取不同部分的细微特征;同时,采用可学习仿射变换改进相似度打分模块,使模型学习到了更多细微的差异,进一步提高了案例匹配的性能。在CAIL2019-SCM数据集上的实验结果表明,本文提出方法与现有方法相比准确率提升了1.89%。

关键词: 相似案例匹配, 文本匹配, 法律智能, 卷积, 仿射变换

Abstract: Similarity case matching(SCM)task is to judge whether the cases described in two judgment documents are similar. SCM is usually regarded as the text matching problem of judgment documents and has important applications in the judicial trial. Existing deep learning models mostly encode long texts of cases into a single vector, and it is difficult for the model to learn the subtle differences between the cases from long texts. Considering that the content of each part of the case text is relatively fixed, this paper proposes to split the long case text into multiple pieces and encode them separately to obtain the subtle features of different parts. At the same time, learnable affine-transformation is used to improve the similarity scoring module, so that the model learn more subtle differences, which further improves the performance of case matching. The experimental results on the CAIL2019-SCM data set show that compared to another model, the accuracy of the method proposed in this paper have increased by 1.89%.

中图分类号:

TP391

赖华,张恒滔,线岩团,黄于欣. 融合分段编码与仿射机制的相似案例匹配方法[J]. 《山东大学学报(理学版)》, 2023, 58(1): 40-47.

LAI Hua, ZHANG Heng-tao, XIAN Yan-tuan, HUANG Yu-xin. A similarity case matching method combining segment encoding and affine-mechanism[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(1): 40-47.

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融合分段编码与仿射机制的相似案例匹配方法

A similarity case matching method combining segment encoding and affine-mechanism

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