孟加拉语_MER:为孟加拉语数学实体识别提供独特的数据集
Tanjim Taharat Aurpa1, Samiha Maisha Jeba2, Md Shoaib Ahmed3,4
1Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh.
Data in brief
|May 6, 2024
概括
这项研究介绍了机器学习的第一个孟加拉语数学实体数据集. 它有助于识别孟加拉语中的数学术语,运算符和数字,推进自然语言处理.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 数学实体识别对于数学内容的计算理解至关重要.
- 孟加拉语数学实体识别的现有资源很少,阻碍了研究和开发.
- 需要专门的数据集来推进孟加拉语数学表达式的机器学习模型.
研究的目的:
- 引入孟加拉语数学实体识别的第一个全面数据集.
- 为了促进研究识别数学运算符,术语和操作数在孟加拉语.
- 支持开发高级机器学习和深度学习模型,用于孟加拉语数学.
主要方法:
- 编制了一个新的数据集,包括13,717个观测.
- 每个记录包括一个数学语句,它的类型,以及识别的数学实体.
- 数据集以原始格式结构化,并以CSV文件的形式提供,其中包含"文本"",数学实体"和"标签"列.
主要成果:
- 创建一个最先进的孟加拉语数学实体数据集.
- 数据集可以识别运算符,数学术语 (如复数) 和数值运算符.
- 该数据集可用于研究微小修改后的合并实体认可.
结论:
- 这一数据集对孟加拉自然语言处理和人工智能研究做出了重大贡献.
- 它为开发和评估在孟加拉语中用于数学实体识别的机器学习模型提供了基础.
- 研究人员可以利用这个数据集在深度学习和机器学习应用到数学语言的各种探索.
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