深度亚拉姆语:朝着一个合成数据范式,使机器学习能够在书面写作中实现.
Andrei C Aioanei1, Regine R Hunziker-Rodewald1, Konstantin M Klein2
1Faculty of Theology and Religious Science, University of Strasbourg, Strasbourg, France.
PloS one
|April 19, 2024
概括
研究人员开发了一种方法来创建用于机器学习 (ML) 的合成古亚兰文字数据. 这种方法训练模型准确阅读受损的古文铭文,克服了书写学中的数据稀缺性.
科学领域:
- 数字人文学科 数字人文学科
- 计算语言学 计算语言学
- 考古学中的人工智能
背景情况:
- 书面写作面临着有限的标记数据的挑战,用于训练机器学习 (ML) 算法,特别是在古亚拉姆语等古代脚本中.
- 目前的ML技术受到现实世界铭文数据稀缺的限制,阻碍了对受损历史文本的分析.
研究的目的:
- 开创一种新的方法来生成专门针对古亚拉姆文字的合成训练数据.
- 为了克服标记数据稀缺所造成的局限性,在史诗分析中.
- 为了提高解读损坏的古代铭文的准确性.
主要方法:
- 开发了一个管道来合成照片现实的旧亚拉姆字母数据集,结合各种纹理特征,照明条件,损坏和增强.
- 设计了一大堆25万个培训和25000个验证图像,涵盖所有22个阿拉姆字母.
- 在合成数据集上训练了一个残余神经网络 (ResNet) 模型,用于分类退化的阿拉姆字母.
主要成果:
- ResNet模型在从公元前8世纪的铭文中分类真正的古亚拉姆文字时达到95%的准确性.
- 验证了模型在不同的材料和风格之间有效的概括性.
- 证明了该模型能够处理各种现实世界的场景,并具有退化的铭文.
结论:
- 合成数据生成方法是可行的,并且有效地克服了依赖书面写作中的稀缺培训数据的依赖.
- 创新的框架显著提高了对损坏的铭文的解释准确性.
- 这种方法提升了从历史史记资源中提取知识的方法.
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