Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2932-2939.DOI: 10.11772/j.issn.1001-9081.2022081163
• Multimedia computing and computer simulation • Previous Articles Next Articles
Ruiqi WANG, Shujuan JI(), Ning CAO, Yajie GUO
Received:
2022-08-08
Revised:
2023-01-07
Accepted:
2023-01-16
Online:
2023-09-10
Published:
2023-09-10
Contact:
Shujuan JI
About author:
WANG Ruiqi, born in 1997, M. S. candidate. Her research interests include artificial intelligence.Supported by:
通讯作者:
纪淑娟
作者简介:
王瑞琪(1997—),女,山东菏泽人,硕士研究生,主要研究方向:人工智能基金资助:
CLC Number:
Ruiqi WANG, Shujuan JI, Ning CAO, Yajie GUO. Semi-supervised fake job advertisement detection model based on consistency training[J]. Journal of Computer Applications, 2023, 43(9): 2932-2939.
王瑞琪, 纪淑娟, 曹宁, 郭亚杰. 基于一致性训练的半监督虚假招聘广告检测模型[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2932-2939.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022081163
数据集 | 所属领域 | 真实/积极样本数 | 欺诈/消极样本数 |
---|---|---|---|
EMSCAD | 招聘广告文本 | 17 014 | 866 |
IMDB | 电影评论文本 | 25 000 | 25 000 |
Tab. 1 Distribution of original datasets
数据集 | 所属领域 | 真实/积极样本数 | 欺诈/消极样本数 |
---|---|---|---|
EMSCAD | 招聘广告文本 | 17 014 | 866 |
IMDB | 电影评论文本 | 25 000 | 25 000 |
名称 | 属性描述 | 属性类型 |
---|---|---|
title | 广告标题 | 文本特征 |
location | 广告地理位置 | 文本特征 |
department | 公司内部部门 | 文本特征 |
salary_range | 薪资范围 | 数字特征 |
company_profile | 公司简介 | 文本特征 |
description | 招聘广告描述 | 文本特征 |
requirements | 工作要求 | 文本特征 |
benefits | 工作福利 | 文本特征 |
telecommuting | 是否远程办公 | 数字特征 |
has_company_logo | 是否有公司logo | 数字特征 |
employment_type | 就业类型 | 文本特征 |
required_experience | 所需的工作经验 | 文本特征 |
required_education | 所需的教育水平 | 文本特征 |
industry | 公司所属领域 | 文本特征 |
function | 岗位的作用 | 文本特征 |
fraudulent | 欺诈与否 | 数字特征 |
Tab. 2 Detailed information of EMSCAD
名称 | 属性描述 | 属性类型 |
---|---|---|
title | 广告标题 | 文本特征 |
location | 广告地理位置 | 文本特征 |
department | 公司内部部门 | 文本特征 |
salary_range | 薪资范围 | 数字特征 |
company_profile | 公司简介 | 文本特征 |
description | 招聘广告描述 | 文本特征 |
requirements | 工作要求 | 文本特征 |
benefits | 工作福利 | 文本特征 |
telecommuting | 是否远程办公 | 数字特征 |
has_company_logo | 是否有公司logo | 数字特征 |
employment_type | 就业类型 | 文本特征 |
required_experience | 所需的工作经验 | 文本特征 |
required_education | 所需的教育水平 | 文本特征 |
industry | 公司所属领域 | 文本特征 |
function | 岗位的作用 | 文本特征 |
fraudulent | 欺诈与否 | 数字特征 |
名称 | 属性描述 | 属性类型 |
---|---|---|
review | 电影评论 | 文本特征 |
sentiment | 情感极性 | 数字特征 |
Tab. 3 Detailed information of IMDB
名称 | 属性描述 | 属性类型 |
---|---|---|
review | 电影评论 | 文本特征 |
sentiment | 情感极性 | 数字特征 |
数据集 | 有标签样本数 | 无标签样本数 | 监督样本数占比/% |
---|---|---|---|
EMSCAD | 20 | 1 732 | 1.10 |
100 | 1 732 | 5.40 | |
200 | 1 732 | 10.30 | |
300 | 1 732 | 14.70 | |
400 | 1 732 | 18.70 | |
20 | 17 880 | 0.10 | |
IMDB | 20 | 20 000 | 0.10 |
100 | 20 000 | 0.50 | |
200 | 20 000 | 1.00 | |
300 | 20 000 | 1.50 | |
400 | 20 000 | 2.00 | |
20 | 50 000 | 0.03 |
Tab. 4 Datasets and distributions based on EMSCAD and IMDB
数据集 | 有标签样本数 | 无标签样本数 | 监督样本数占比/% |
---|---|---|---|
EMSCAD | 20 | 1 732 | 1.10 |
100 | 1 732 | 5.40 | |
200 | 1 732 | 10.30 | |
300 | 1 732 | 14.70 | |
400 | 1 732 | 18.70 | |
20 | 17 880 | 0.10 | |
IMDB | 20 | 20 000 | 0.10 |
100 | 20 000 | 0.50 | |
200 | 20 000 | 1.00 | |
300 | 20 000 | 1.50 | |
400 | 20 000 | 2.00 | |
20 | 50 000 | 0.03 |
参数 | 取值 | 物理意义 |
---|---|---|
学习率 | 1E-5(EMSCAD), 2E-5(IMDB) | 学习率 |
序列最大长度 | 128 | 序列最大长度 |
Dropout | 0.3 | 神经单元失效的概率 |
TSA | exp_schedule | 减轻标签数据较少 过拟合造成的影响 |
λ | 4 | 控制双向KL散度所占比重 |
Tab. 5 Experimental parameter setting
参数 | 取值 | 物理意义 |
---|---|---|
学习率 | 1E-5(EMSCAD), 2E-5(IMDB) | 学习率 |
序列最大长度 | 128 | 序列最大长度 |
Dropout | 0.3 | 神经单元失效的概率 |
TSA | exp_schedule | 减轻标签数据较少 过拟合造成的影响 |
λ | 4 | 控制双向KL散度所占比重 |
标签数 | 类型 | 模型 | EMSCAD | IMDB | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc | P | R | F1 | Acc | P | R | F1 | |||
20 | 监督 学习 | 随机森林 | 0.636 | 0.663 | 0.636 | 0.621 | 0.557 | 0.573 | 0.557 | 0.550 |
SVM | 0.646 | 0.647 | 0.646 | 0.645 | 0.524 | 0.524 | 0.524 | 0.523 | ||
BERT | 0.666 | 0.677 | 0.666 | 0.661 | 0.600 | 0.605 | 0.600 | 0.594 | ||
半监督 学习 | UDA | 0.678 | 0.699 | 0.678 | 0.669 | 0.689 | 0.698 | 0.689 | 0.685 | |
SSC | 0.700 | 0.717 | 0.700 | 0.695 | 0.717 | 0.719 | 0.717 | 0.716 | ||
100 | 监督 学习 | 随机森林 | 0.771 | 0.772 | 0.771 | 0.771 | 0.644 | 0.645 | 0.644 | 0.644 |
SVM | 0.719 | 0.722 | 0.719 | 0.719 | 0.602 | 0.607 | 0.602 | 0.600 | ||
BERT | 0.800 | 0.801 | 0.800 | 0.800 | 0.741 | 0.751 | 0.741 | 0.738 | ||
半监督 学习 | UDA | 0.825 | 0.838 | 0.825 | 0.823 | 0.779 | 0.789 | 0.779 | 0.777 | |
SSC | 0.839 | 0.848 | 0.839 | 0.838 | 0.792 | 0.792 | 0.792 | 0.791 | ||
200 | 监督 学习 | 随机森林 | 0.829 | 0.829 | 0.829 | 0.829 | 0.722 | 0.724 | 0.722 | 0.721 |
SVM | 0.781 | 0.781 | 0.781 | 0.781 | 0.675 | 0.677 | 0.676 | 0.675 | ||
BERT | 0.865 | 0.864 | 0.865 | 0.865 | 0.796 | 0.800 | 0.796 | 0.796 | ||
半监督 学习 | UDA | 0.871 | 0.873 | 0.871 | 0.871 | 0.817 | 0.817 | 0.817 | 0.817 | |
SSC | 0.888 | 0.888 | 0.888 | 0.888 | 0.824 | 0.826 | 0.824 | 0.823 | ||
300 | 监督 学习 | 随机森林 | 0.879 | 0.879 | 0.879 | 0.879 | 0.743 | 0.745 | 0.743 | 0.742 |
SVM | 0.847 | 0.847 | 0.847 | 0.847 | 0.713 | 0.716 | 0.713 | 0.712 | ||
BERT | 0.895 | 0.896 | 0.895 | 0.895 | 0.822 | 0.823 | 0.822 | 0.822 | ||
半监督 学习 | UDA | 0.908 | 0.909 | 0.908 | 0.908 | 0.832 | 0.833 | 0.832 | 0.832 | |
SSC | 0.915 | 0.915 | 0.915 | 0.915 | 0.840 | 0.841 | 0.840 | 0.840 | ||
400 | 监督 学习 | 随机森林 | 0.903 | 0.903 | 0.903 | 0.903 | 0.760 | 0.761 | 0.760 | 0.759 |
SVM | 0.885 | 0.887 | 0.885 | 0.884 | 0.734 | 0.737 | 0.734 | 0.734 | ||
BERT | 0.918 | 0.918 | 0.908 | 0.908 | 0.843 | 0.845 | 0.844 | 0.844 | ||
半监督 学习 | UDA | 0.923 | 0.923 | 0.923 | 0.923 | 0.848 | 0.849 | 0.848 | 0.848 | |
SSC | 0.926 | 0.926 | 0.926 | 0.926 | 0.851 | 0.852 | 0.851 | 0.851 |
Tab. 6 Experimental results with different number of labels on EMSCAD and IMDB
标签数 | 类型 | 模型 | EMSCAD | IMDB | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc | P | R | F1 | Acc | P | R | F1 | |||
20 | 监督 学习 | 随机森林 | 0.636 | 0.663 | 0.636 | 0.621 | 0.557 | 0.573 | 0.557 | 0.550 |
SVM | 0.646 | 0.647 | 0.646 | 0.645 | 0.524 | 0.524 | 0.524 | 0.523 | ||
BERT | 0.666 | 0.677 | 0.666 | 0.661 | 0.600 | 0.605 | 0.600 | 0.594 | ||
半监督 学习 | UDA | 0.678 | 0.699 | 0.678 | 0.669 | 0.689 | 0.698 | 0.689 | 0.685 | |
SSC | 0.700 | 0.717 | 0.700 | 0.695 | 0.717 | 0.719 | 0.717 | 0.716 | ||
100 | 监督 学习 | 随机森林 | 0.771 | 0.772 | 0.771 | 0.771 | 0.644 | 0.645 | 0.644 | 0.644 |
SVM | 0.719 | 0.722 | 0.719 | 0.719 | 0.602 | 0.607 | 0.602 | 0.600 | ||
BERT | 0.800 | 0.801 | 0.800 | 0.800 | 0.741 | 0.751 | 0.741 | 0.738 | ||
半监督 学习 | UDA | 0.825 | 0.838 | 0.825 | 0.823 | 0.779 | 0.789 | 0.779 | 0.777 | |
SSC | 0.839 | 0.848 | 0.839 | 0.838 | 0.792 | 0.792 | 0.792 | 0.791 | ||
200 | 监督 学习 | 随机森林 | 0.829 | 0.829 | 0.829 | 0.829 | 0.722 | 0.724 | 0.722 | 0.721 |
SVM | 0.781 | 0.781 | 0.781 | 0.781 | 0.675 | 0.677 | 0.676 | 0.675 | ||
BERT | 0.865 | 0.864 | 0.865 | 0.865 | 0.796 | 0.800 | 0.796 | 0.796 | ||
半监督 学习 | UDA | 0.871 | 0.873 | 0.871 | 0.871 | 0.817 | 0.817 | 0.817 | 0.817 | |
SSC | 0.888 | 0.888 | 0.888 | 0.888 | 0.824 | 0.826 | 0.824 | 0.823 | ||
300 | 监督 学习 | 随机森林 | 0.879 | 0.879 | 0.879 | 0.879 | 0.743 | 0.745 | 0.743 | 0.742 |
SVM | 0.847 | 0.847 | 0.847 | 0.847 | 0.713 | 0.716 | 0.713 | 0.712 | ||
BERT | 0.895 | 0.896 | 0.895 | 0.895 | 0.822 | 0.823 | 0.822 | 0.822 | ||
半监督 学习 | UDA | 0.908 | 0.909 | 0.908 | 0.908 | 0.832 | 0.833 | 0.832 | 0.832 | |
SSC | 0.915 | 0.915 | 0.915 | 0.915 | 0.840 | 0.841 | 0.840 | 0.840 | ||
400 | 监督 学习 | 随机森林 | 0.903 | 0.903 | 0.903 | 0.903 | 0.760 | 0.761 | 0.760 | 0.759 |
SVM | 0.885 | 0.887 | 0.885 | 0.884 | 0.734 | 0.737 | 0.734 | 0.734 | ||
BERT | 0.918 | 0.918 | 0.908 | 0.908 | 0.843 | 0.845 | 0.844 | 0.844 | ||
半监督 学习 | UDA | 0.923 | 0.923 | 0.923 | 0.923 | 0.848 | 0.849 | 0.848 | 0.848 | |
SSC | 0.926 | 0.926 | 0.926 | 0.926 | 0.851 | 0.852 | 0.851 | 0.851 |
模型 | EMSCAD | IMDB | ||||||
---|---|---|---|---|---|---|---|---|
Acc | P | R | F1 | Acc | P | R | F1 | |
UDA | 0.715 | 0.716 | 0.715 | 0.714 | 0.842 | 0.844 | 0.842 | 0.842 |
SSC | 0.735 | 0.738 | 0.735 | 0.734 | 0.869 | 0.870 | 0.869 | 0.869 |
Tab. 7 Comparison of UDA and SSC results on original datasets when number of labels is 20
模型 | EMSCAD | IMDB | ||||||
---|---|---|---|---|---|---|---|---|
Acc | P | R | F1 | Acc | P | R | F1 | |
UDA | 0.715 | 0.716 | 0.715 | 0.714 | 0.842 | 0.844 | 0.842 | 0.842 |
SSC | 0.735 | 0.738 | 0.735 | 0.734 | 0.869 | 0.870 | 0.869 | 0.869 |
类型 | 模型 | 不同数据集上的运行时间/s | |
---|---|---|---|
EMSCAD | IMDB | ||
监督学习 | 随机森林 | 1.2×10-1 | 1.9×10-1 |
SVM | 3.7×10-2 | 1.0×10-1 | |
BERT | 1.4×103 | 2.5×103 | |
半监督学习 | UDA | 3.5×103 | 4.6×103 |
SSC | 7.7×103 | 8.9×103 |
Tab. 8 Comparative analysis of time efficiency
类型 | 模型 | 不同数据集上的运行时间/s | |
---|---|---|---|
EMSCAD | IMDB | ||
监督学习 | 随机森林 | 1.2×10-1 | 1.9×10-1 |
SVM | 3.7×10-2 | 1.0×10-1 | |
BERT | 1.4×103 | 2.5×103 | |
半监督学习 | UDA | 3.5×103 | 4.6×103 |
SSC | 7.7×103 | 8.9×103 |
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