孟加拉语Tense:大规模的孟加拉语句子数据集,按时分类:过去,现在和未来
Md Hasan Imam Bijoy1, Umme Ayman1, Md Monarul Islam1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Data in brief
|March 19, 2025
概括
孟加拉语文数据集提供了大量的孟加拉语句子,按过去,现在和未来时代分类. 本资源有助于开发孟加拉语的自然语言处理 (NLP) 模型.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 孟加拉语语言资源
背景情况:
- 孟加拉语是一门印阿语语言,具有复杂的语法结构,特别是其时态系统.
- 孟加拉语现有的自然语言处理 (NLP) 资源有限,阻碍了语言技术的进步.
- 准确的时间分析对于各种NLP应用是必不可少的.
研究的目的:
- 介绍BanglaTense数据集,这是一个新的,大规模的资源,用于孟加拉语句子时态分类.
- 为了解决在NLP任务中注释的孟加拉语数据稀缺的问题.
- 为评估孟加拉语时间分析NLP模型提供一个基准.
主要方法:
- 一个精心策划的17819个孟加拉语句子的集合被编译出来.
- 句子被注释成三个时代:过去 (5,629),现在 (6,101) 和未来 (6,089).
- 数据预处理包括匿名化和重复删除,紧张标签由三个母语者验证.
主要成果:
- 孟加拉语数据集包括17,819个句子,在过去,现在和未来时代中得到平衡.
- 数据集经过严格的质量控制,包括母语验证,确保高可靠性.
- 该资源是公开可用的,以促进孟加拉语NLP的研究和开发.
结论:
- 孟加拉语Tense是推动孟加拉语NLP的基础资源,特别是在时态检测和句子分类方面.
- 该数据集促进了语言多样性和开发更具包容性和准确的语言模型.
- 预计其可用性将刺激孟加拉语NLP应用程序和教育工具的创新.
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