警报:一个基准的孟加拉语数据集,用于识别和分类具有宗教侵略性的文本
Suhana Binta Rashid1, Bibhas Roy Chowdhury Piyas2, Sadia Rahman1
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh.
Data in brief
|October 15, 2025
概括
创建了一个新的孟加拉语数据集,ALERT,以检测在线具有宗教侵略性的内容. 该资源旨在改进孟加拉语的检测工具,解决当前研究中的差距,并促进更安全的在线空间.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 自然语言处理自然语言处理.
背景情况:
- 社交媒体上的宗教攻击性内容威胁到社会和.
- 现有的检测此类内容的工具仅限于孟加拉语等区域语言.
- 需要资源来制定有效的检测和预防策略.
研究的目的:
- 介绍ALERT,一个新的孟加拉语数据集用于宗教攻击性文本分类.
- 为了解决分析孟加拉语极端主义内容的资源短缺问题.
- 促进开发用于识别和减轻有害在线内容的工具.
主要方法:
- 创建了ALERT数据集,包含四个类别的4027个注释实例:仇恨言论,破坏,暴行和没有侵略.
- 来自各种在线平台 (Facebook,YouTube,博客等) 的数据来源. ) 的情况.
- 通过不同的注释者进行注释,由一个领域专家进行冲突解决;预处理包括数据清理和完整性检查.
主要成果:
- ALERT数据集显示了强烈的注释者间共识 (科恩的kappa得分为72%).
- 机器学习,深度学习和变压器模型的实验显示了对文本分类的有希望的结果.
- 该数据集为宗教攻击性文本的特征和分类提供了宝贵的见解.
结论:
- ALERT 作为孟加拉语宗教攻击性文本分类的基准数据集.
- 数据集的公开可访问性促进了孟加拉语NLP的研究,创新和协作.
- 预计这项资源将促进对抗在线宗教极端主义的工具的开发.
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