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scigen-detection's Introduction

一、预处理

  • 将待测试的文档(txt文件)放在testpaper目录下
  • 运行txtBeautiful.py,进行预处理

二、使用词同现网络判别

  • 运行calFeature.py,构建词同现网络并且计算出特征
  • 运行judge_network.py进行判别

三、使用bow进行判别

  • 运行judge_bow.py进行判别即可

四、文件说明

  • calFeature.py : 用于词同现网络构造特征
  • judge_bow.py : bow判别
  • judge_network.py : 词同现网络判别
  • printCluster.py :打印出层次聚类结果,证明bow距离度量的准确性
  • scigenDownLoad.py: 用于爬取scigen论文
  • trainBow.py : 计算Bow准确性
  • trainKnn.py : 计算词同现网络准确性(KNN判别)
  • trainSvm.py : 计算词同现网络准确性(SVM判别)
  • txtBeautiful.py: txt预处理
  • data : 数据文件
    • false: 伪论文(scigen生成)
    • true : 真论文
    • paper.data : 真论文词同现网络特征
    • scigen.data: 伪论文词同现网络特征
    • test.data : 待测论文词同现网络特征
  • testpaper:待测论文存放的文件夹

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