在低资源语言中推进网络欺凌检测:孟加拉语的变压堆叠框架
Md Nesarul Hoque1,2, Rudra Pratap Deb Nath1, Abu Nowshed Chy1
1Big Data, Information and Knowledge Engineering Lab, Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.
Frontiers in artificial intelligence
|January 29, 2026
概括
这项研究介绍了变压器堆叠,这是一个用于在孟加拉社交媒体上检测网络欺凌的新框架. 该模型实现了高精度,为低资源语言设定了新的基准.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 社交计算社会计算
背景情况:
- 网络欺凌是一个全球性问题,由于数据和方法的稀缺,孟加拉语等低资源语言的研究有限.
- 现有的方法往往缺乏特定语言的预处理和先进的变压器模型,阻碍了有效的网络欺凌检测.
研究的目的:
- 开发和评估一个有效的混合框架,用于孟加拉网络欺凌检测.
- 通过结合孟加拉特有的预处理和先进的变压器模型来解决当前方法的局限性.
主要方法:
- 引入了三个针对孟加拉语的预处理策略,以改善特征表示.
- 拟议的变压器堆叠,一个混合框架,结合XLM-R基础,多语言BERT和Bangla-Bert-Base与多层感知子分类器.
- 评估了对44,001个样本的孟加拉网络欺凌数据集的框架,用于二进制和多类分类.
主要成果:
- 变压器堆叠实现了93.61%的F1分数和93.62%的二进制分类准确度 (A子任务).
- 获得了89.23%的F1分数和多类分类的准确性 (子任务B).
- 超越基线模型,组合技术和最先进的方法,结果通过麦克纳马的测试得到统计验证.
结论:
- 变压器堆叠为孟加拉语网络欺凌检测提供了有效和可通用的解决方案.
- 这项研究建立了一个新的基准,用于在未经探索的低资源语言中检测网络欺凌.
- 通过对仇恨言论和滥用语言检测外部数据集的实验,证明了模型的可扩展性和适应性.
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