BTSD:对文本分类的句子数据集进行了精选的转换,用于孟加拉语的文本分类
Rajesh Kumar Das1, Mirajul Islam1, Sharun Akter Khushbu1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka 1341, Bangladesh.
Data in brief
|August 14, 2023
概括
本研究介绍了Bangla Transformation of Sentence Classification数据集,这是自然语言处理 (NLP) 任务的宝贵资源. 它有助于开发更好的孟加拉语句子分类模型.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 孟加拉语语言学研究
背景情况:
- 孟加拉语在NLP数据集中面临着严重的资源缺口,这阻碍了对特定语言的AI模型的开发.
- 现有的NLP资源往往缺乏足够的数据来完成细微的任务,例如在孟加拉语中对句子进行分类.
- 促进人工智能的语言多样性需要为资源不足的语言提供专门的数据集.
研究的目的:
- 介绍和描述孟加拉语句子分类转换数据集.
- 为评估孟加拉语句子分类的NLP模型提供一个基准.
- 促进研究和开发孟加拉语特定的NLP应用程序.
主要方法:
- 一个包含3793个孟加拉语句子的数据集被策划和注释.
- 句子被分为简单,复杂和复合的类别.
- 数据从公开的Facebook页面上收集,匿名化,并删除重复.
- 标签可靠性是通过三个孟加拉语母语者进行独立评估来确保的.
主要成果:
- 数据集提供了跨简单,复杂和复合类的孟加拉语句子的平衡表示.
- 它建立了孟加拉语句子分类性能的基准.
- 该数据集促进了孟加拉语NLP模型的培训和评估.
结论:
- 孟加拉语句子分类转换数据集解决了孟加拉语NLP的关键资源缺口.
- 它使研究人员和开发人员能够构建更准确,更强大的孟加拉语语言模型.
- 该数据集有助于孟加拉语语法语言研究,并促进包容性AI发展.
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