普罗什诺·宾纳什:用于低资源NLP的文本式多标签问答数据集
Rajib Khan1, Tanjim Taharat Aurpa2, Md Saidur Rahman1
1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka-1230, Bangladesh.
Data in brief
|December 24, 2025
概括
PROSHNO BINNASH是一个新的孟加拉语问答数据集,用于低资源自然语言处理 (NLP). 这种人为生成的数据集有助于为孟加拉语使用者开发AI教育工具.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 计算语言学 计算语言学
背景情况:
- 越来越多的人对孟加拉语自动学习工具的需求.
- 在孟加拉语中,对于低资源自然语言处理 (NLP) 任务的资源供应有限.
- 需要专门的数据集来推进孟加拉语说者的AI驱动的教育技术.
研究的目的:
- 介绍PROSHNO BINNASH,一个新的语境多标签问答数据集,用于孟加拉语NLP.
- 为教育应用解决孟加拉语NLP资源的缺口.
- 促进为孟加拉语使用者开发包容性AI驱动的教育工具.
主要方法:
- 从本质的孟加拉文本中策划了4069个条目,包括文学和历史叙事.
- 标注每个条目与上下文,问题,答案和十个类别的多标签分类.
- 通过专家对上下文,问题和答案的注释,确保准确性和相关性.
主要成果:
- 开发了PROSHNO BINNASH,一个独特的数据集,为孟加拉语问答提供多标签注释.
- 数据集包括十个类别:体育,健康和运动,文学,著名人物,电影,科学,解放战争,政治,艺术家和历史.
- 数据集支持问题和类之间的一对多关系,反映了教育内容的复杂性.
结论:
- 在孟加拉语NLP研究中,PROSHNO BINNASH满足了关键的需求.
- 该数据集对教育,孟加拉语分析,多标签分类和学术聊天机器人开发有价值.
- 为数以百万计的孟加拉语使用者提供AI驱动的教育技术的进步做出贡献.
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