中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

Journal of Chongqing Jiaotong University(Natural Science) ›› 2004, Vol. 23 ›› Issue (1): 116-120.

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Developing of the law-case automatic categorizing system based on law lexicons

GUAN Li-he1 ,  YANG Gang2 ,LI Yong-li2   

  1. 1.Department of Computera and Information, Chongqing Jiaotong University, Chongqing 400074,China; 2.School of Information Science and Engineering, Lanzhou University, Gansu Lanzhou 730000,China
  • Received:2003-02-20 Revised:2003-05-22 Online:2004-02-15 Published:2015-05-18

基于词典的法律案例自动归类系统的开发

官礼和,1杨刚2,李永礼2   

  1. 1.重庆交通学院计算机与信息学院,重庆400074;2.兰州大学信息科学与工程学院,甘肃兰州730000
  • 作者简介:官礼和(1975-),男,四川乐山人,硕士研究生,主要从事人工智能、神经网络、数据挖掘方面研究

Abstract: The paper discussed and developed a Law-case automatic categorizing system, which makeis a subsystem of the "Law-case Analyzing System".Firstly,the characteristic-word weight tables of each category are gained out of a mass of law-case training documents which have been categorized already.Secondly,the weight summation is conducted for each category based on the weight tables of the characteristic Words.Finally,the related law case falls under the category which is at the leafage of the Category

Key words: sum weight, characteristic words, characteristic-word weight tables, word frequency, characteristic lexicons

摘要: 笔者详细讨论并成功开发了“法律案例分析系统”的一个子系统—“法律案例自动归类系统”.系统首先通过 大量的法律案例训练文档得到树结构中每个类(叶子类和中间类)的类特征词权值表,然后在此基础上计算新法律 案例文档相对于各个类的累加权值,最后累加权值最大并且是叶子类的类即是该法律案例应归入的类.笔者还给 出并分析了用到的两个重要公式(特征词权值公式和类累加权值公式),详细介绍了系统的核心—基于词典的分词 算法.实验表明本系统具有很好的通用性和扩展性,归类准确率较理想

关键词: 累加权值, 特征词, 类特征词权值表, 词频, 特征词典

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