OLTW-TEC:用于文本分类器组合的移动窗口的在线学习
Khrystyna Lipianina-Honcharenko1, Yevgeniy Bodyanskiy2, Nataliia Kustra3
1Department of Information Computer Systems and Control, West Ukrainian National University, Ternopil, Ukraine.
Frontiers in artificial intelligence
|September 26, 2024
概括
一种新的机器学习方法,即用于文本分类组合的滑动窗口在线学习 (OLTW-TEC),可以有效地检测乌克兰文本中的虚假信息. 这种可适应的系统实现了93%的准确性,在地缘政治紧张局势下增强了数字信息完整性.
科学领域:
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
背景情况:
- 虚假信息在数字时代构成了重大挑战,特别是在地缘政治热点地区.
- 由于正在进行的混合战争,乌克兰的信息空间受到虚假信息的严重影响.
- 现有的检测方法很难适应不断发展的虚假信息策略.
研究的目的:
- 开发和验证一种先进的机器学习方法,用于检测乌克兰语文本中的虚假信息.
- 创建一个灵活,高效和高度准确的实时虚假信息识别系统.
- 在一个动态的信息环境中,迫切需要对抗假新闻的工具.
主要方法:
- 介绍使用文字分类器组合 (OLTW-TEC) 的滑动窗口在线学习方法.
- 使用一组分类器与滑动窗口技术相结合,用于持续更新模型.
- 评估使用一个独特的数据集真实和假的乌克兰新闻项目,精确分析,回忆和F1得分.
主要成果:
- 通过OLTW-TEC方法实现了93%的特殊分类准确度.
- 与分类器相结合的滑动窗口技术显著改善了虚假信息的识别.
- 该系统在检测假新闻方面表现出了强度和适应性.
结论:
- OLTW-TEC是乌克兰虚假信息检测的多功能和有效解决方案.
- 该方法的适应性为开发其他语言和地区的类似工具提供了洞察力.
- 这项研究强调了创新机器学习在维护数字信息完整性方面的关键作用.
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