相关实验视频
孟加拉-ToCo:孟加拉语有毒评论检测上下文感知数据集
Sayma Akter Rupa1, Md Musfique Anwar2, Nadia Afrin Ritu2
1R.P. Shaha University, Bangladesh.
Data in brief
|December 17, 2025
概括
本研究介绍了第一个上下文感知的孟加拉语有毒评论数据集,这对于开发自动调节系统至关重要. 数据集有助于理解在线虐待在孟加拉语,一个低资源语言,通过保留对话上下文.
科学领域:
- 计算语言学 计算语言学
- 社会科学 社会科学 社会科学
- 自然语言处理自然语言处理.
背景情况:
- 滥用和有毒的评论在网上越来越令人担忧,特别是在孟加拉语等低资源语言中.
- 孟加拉语的现有资源缺乏对话上下文,阻碍了对在线评论的准确分析.
- 孟加拉语社交媒体需要可靠的自动调节系统.
研究的目的:
- 引入一套新的上下文感知数据集,用于识别孟加拉语的有毒评论.
- 通过保留对话上下文,使评论能够更准确地解释.
- 支持为孟加拉语开发NLP工具,这是一个资源较低的语言.
主要方法:
- 从孟加拉国新闻门户网站的Facebook页面收集了1004个孟加拉新闻评论.
- 通过包括新闻标题,文章元数据和周围评论来保存对话上下文.
- 在"有毒"和"无毒"类别中使用独立的人类注释器和多数投票进行注释评论.
主要成果:
- 开发了第一个具有上下文意识的孟加拉语有毒评论数据集.
- 创建了一个平衡的数据集,适合监督学习和基准测试.
- 数据集包括新闻标题,元数据,目标评论和对话背景.
结论:
- 这一数据集是孟加拉语NLP和社会话语分析的宝贵资源.
- 它促进了对孟加拉语的滥用内容检测和情绪分析的研究.
- 情境意识的方法提高了对在线毒性的理解,在低资源语言.
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