基于深度学习机器的阿拉伯商业电子邮件分类的新方法
Aladdin Masri1, Muhannad Al-Jabi1
1Computer Engineering Department, An-Najah National University, Nablus, Palestine.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了自然语言处理 (NLP) 模型,用于根据紧迫性,情绪和主题对阿拉伯商业电子邮件进行分类. 开发的卷积神经网络 (CNN) 模型实现了超过92%的准确性,证明了有效的阿拉伯文本分类.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 越来越多的商业电子邮件需要有效的内容分类.
- 阿拉伯文本分类仍然是一个研究不足的领域,尽管它在官方通信中的重要性越来越大.
- 现有的电子邮件分类算法往往缺乏对阿拉伯语内容的专业支持.
研究的目的:
- 开发和评估用于分类阿拉伯商业电子邮件的机器学习模型.
- 在商业环境中解决阿拉伯文本分类的具体挑战.
- 根据紧迫性,情绪和主题对电子邮件进行分类.
主要方法:
- 利用了63,257封阿拉伯商业电子邮件的大数据集.
- 使用自然语言处理 (NLP) 技术与机器学习相结合.
- 开发并测试了多个卷积神经网络 (CNN) 模型,每个模型都是针对特定的分类任务 (紧急性,情绪,主题) 量身定制的.
- 加入了词汇词典,以增强电子邮件的识别和分类.
主要成果:
- 在所有分类任务中实现了高精度,超过92%.
- 保持低损失率,低于8%,表明模型性能强.
- 证明了CNN模型对阿拉伯电子邮件分类的有效性.
- 验证了拟议的分类方法的正确性和稳定性.
结论:
- 开发的NLP和CNN模型为阿拉伯商业电子邮件的分类提供了高度准确的解决方案.
- 这项研究对阿拉伯文本分类领域做出了重大贡献.
- 这些发现支持这些模型在管理大量商业通信时的实际应用.
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