孟加拉语数学实体识别 (MER) 的整体新型架构使用基于变压器的学习
Tanjim Taharat Aurpa1, Md Shoaib Ahmed2,3
1Department of Data Science, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh.
Heliyon
|February 15, 2024
概括
本研究介绍了使用变压器 (BERT) 的双向编码器表示的孟加拉语数学实体识别 (MER). 这种新的方法在识别数学运算符,操作数和英语术语方面取得了高准确性.
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:
- 数学实体识别对于机器理解和处理数学内容至关重要.
- 应用包括自动定理证明,知识检索和教育平台.
- 孟加拉语中的数学实体识别是一个尚未探索的领域.
研究的目的:
- 开发和评估孟加拉语数学实体识别 (MER) 的系统.
- 识别数学运算符,操作数 (数字) 和常见的数学术语.
- 为了实现这一任务,利用深度学习,特别是来自变压器的双向编码器表示 (BERT).
主要方法:
- 利用基于BERT的深度神经网络的整体架构.
- 创建了一个新型数据集,包含13,717个孟加拉语数学语句,并注释了实体和类型.
- 雇佣了准确性,精度,回忆力和F1得分作为绩效指标.
主要成果:
- 使用单一的BERT模型实现了97.98%的令人满意的准确性.
- 整体BERT架构表现出卓越的性能,精度为99.76%.
- 该系统有效地识别了运算符,操作数和数学术语.
结论:
- 拟议的整体BERT模型对孟加拉语数学实体识别非常有效.
- 这项工作为孟加拉语MER建立了基准.
- 开发的数据集和方法可以促进未来的多语言数学NLP研究.
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