一个混合模型与新词权重用于快速过垃圾邮件短文本
Tian Xia1, Xuemin Chen2, Jiacun Wang3
1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了一种混合模型,将人工神经网络 (ANN) 和隐藏的马尔科夫模型 (HMM) 结合起来,以实现高效的短文本分类. 该模型有效地过垃圾邮件,即使使用非正式语言,提供高准确性和速度.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 信息安全 信息安全
背景情况:
- 短短短信 (短信,微博,应用程序) 容易受到垃圾邮件的攻击,因为它们的成本低,覆盖范围广.
- 简短文本的分类是具有挑战性的,因为它们的简洁性,稀疏性,快速演变和非正式语言.
- 像隐藏马尔科夫模型 (HMM) 这样的现有方法与新的,非正式书面单词作斗争.
研究的目的:
- 提出一种混合模型,用于快速准确地过短文本,特别是解决非正式写作的挑战.
- 通过有效加权新词来改善短消息中的垃圾邮件检测.
- 为了提高短文本分类的性能,超越传统的HMM功能.
主要方法:
- 开发了一个混合模型,集成了一个人工神经网络 (ANN) 来进行新词加权和一个HMM用于垃圾邮件过.
- 根据邻近的单词权重和预测的垃圾邮件/垃圾邮件概率,ANN计算新单词权重.
- 在基准数据集上评估业绩,包括短信,电影评论和客户评论.
主要成果:
- 与深度学习模型相比,混合模型表现出明显更高的操作速度.
- 实验结果显示,混合模型在短文本分类中表现优于其他著名的机器学习算法.
- 拟议的模型在过吞吐量和分类准确性之间取得了有利的平衡.
结论:
- 混合ANN-HMM模型有效地解决了短文本分类中的非正式写作挑战.
- 这种方法为短信的高精度,高速垃圾邮件过提供了一个强大的解决方案.
- 该模型为在多种数字通信平台时代对抗垃圾邮件的现有方法提供了优质的替代方案.
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