基于深度变压器的架构,用于识别来自现实世界数学问题的数学方程
Tanjim Taharat Aurpa1, Kazi Noshin Fariha2, Kawser Hossain2
1Bangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh.
Heliyon
|December 6, 2024
概括
本研究引入了一种新的方法,用于使用深度变压器架构从文本中识别数学方程. 伯特实现了99.80%的准确性,在数学内容方面推进了自然语言处理 (NLP).
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.
背景情况:
- 由于复杂的符号和结构,识别文本中的数学方程具有挑战性.
- 传统的NLP和光学字符识别 (OCR) 方法与数学符号作斗争.
研究的目的:
- 开发和评估深度变压器架构,用于准确的数学方程识别.
- 为了解决理解复杂数学表达式的现有方法的局限性.
主要方法:
- 使用了3433个数学方程的新型数据集.
- 应用并比较了基于变压器的模型,包括BERT,ELECTRA,XLNet,RoBERTa和DistilBERT.
- 专注于识别六种基本的数学方程类型.
主要成果:
- 伯特显示出卓越的性能,准确率为99.80%.
- 提出的方法有效地处理数学符号和结构的复杂性.
- 从数学文本中识别方程,取得了最先进的结果.
结论:
- 深度变压器架构,特别是BERT,对于数学方程识别非常有效.
- 这项研究代表了NLP在数学内容理解方面取得的重大进展.
- 开发的方法在学术和学习系统中具有潜在的应用.
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