从历史手稿中分类印阿里语和泰米尔语文本的混合模型
P M Dinesh1, Dhanalakshmi Gopal2, S Vignesh3
1Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India. paramathimanidinesh@yahoo.com.
Scientific reports
|November 27, 2025
概括
一个新的混合人工智能模型准确地分类了古印度亚语和泰米尔手稿. 这种深度学习方法结合了BERT和LiScribe,在历史文档分析中具有很高的准确性.
科学领域:
- 计算语言学 计算语言学
- 数字人文学科 数字人文学科
- 人工智能的人工智能
背景情况:
- 古代印度-阿里亚语和泰米尔语手稿展示了复杂的语言结构和历史变异.
- 这些历史文本的准确分类对于保存和研究至关重要.
研究的目的:
- 开发和评估一种混合人工智能模型,用于分类历史印阿里语和泰米尔语手稿.
- 为了实现高分类准确度,区分这些脚本家族.
主要方法:
- 这是一个混合模型,它结合了BERT (来自变压器的双向编码器表示) 来实现语义理解,以及LiScribe用于字符级语言模式识别.
- 利用了来自国会图书馆和汉堡大学的1,055份手稿的数据集.
- 使用分类交叉损失,Adam优化器,学习速率调度和放弃层进行训练,以防止过拟合.
主要成果:
- 伯特-利的Scribe混合模型实现了97.61%的整体分类准确度.
- 该模型成功地区分了印阿里文 (印地语,孟加拉语,马拉提语,古吉拉提语) 和泰米尔文本.
- 一个注意力机制增强了特征焦点,特别是在受损的手稿部分.
结论:
- 混合深度学习和语言特征提取方法在分类历史手稿方面是有效的.
- 这种方法为分析复杂的历史脚本提供了强大的解决方案.
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