多语言深度学习框架用于使用囊神经网络检测假新闻
Rami Mohawesh1, Sumbal Maqsood2, Qutaibah Althebyan1,3
1Cybersecurity Department, College of Engineering, Al Ain University, Al Ain - Abu Dhabi, UAE.
概括
这项研究引入了一种新的语义方法,用于多语言假新闻检测,在各种语言中表现优于现有的方法. 这项研究利用一个囊神经网络,在不同的语言环境中更好地识别欺骗性内容.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 由于语言的复杂性,多语言假新闻检测带来了重大挑战.
- 现有的方法很难在多语言 corpora 中捕捉到足够的语义和上下文特征.
- 在当今的信息环境中,准确识别假新闻至关重要.
研究的目的:
- 开发一种用于多语言假新闻检测的语义方法.
- 解决当前方法在提取丰富的语言特征方面的局限性.
- 为了提高在不同语言中识别假新闻的准确性.
主要方法:
- 使用基于关系变量 (情绪,实体,事实) 的语义方法.
- 这项研究使用了囊神经网络架构.
- 在TALLIP假新闻数据集上进行了多语言评估的实验.
主要成果:
- 与最先进的方法相比,拟议的模型表现出优越的性能.
- 在英语到英语 (3.97%),英语到印度语 (1.41%),英语到印尼语 (5.47%),英语到斯瓦希里语 (2.18%) 和英语到越南语 (2.88%) 评估中观察到显著的绩效增长.
- 该模型有效地从多语言文本中提取语义和上下文特征.
结论:
- 开发的语义方法为多语言假新闻检测提供了强大的解决方案.
- 囊神经网络显示出在各种语言环境中提升假新闻识别的前景.
- 这项研究代表了应用囊神经网络用于多语言假新闻检测的开创性努力.
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