对PDF文档的知识和信息提取以及存储方法的审查
Salvador D Atagong1,2, Henri Tonnang2,3, Kennedy Senagi1
1Data Management and Geo-Information Unit (DMMGU), International Centre of Insect Physiology and Ecology, Nairobi, Kenya.
Frontiers in artificial intelligence
|September 22, 2025
概括
自动从便携式文档格式 (PDF) 文档中提取信息至关重要. 一个新的框架解决了当前PDF信息提取方法的局限性,提高了准确性和适应性.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
背景情况:
- 自动从便携式文档格式 (PDF) 文档中提取信息,在医疗保健和法律等各个领域提供了显著的好处.
- 当前的PDF信息提取解决方案在准确性,域名适应性和实现复杂性方面扎.
研究的目的:
- 系统地审查PDF信息提取和存储的现有方法和趋势.
- 提出一种新的概念框架,以克服当前方法的局限性.
主要方法:
- 按照系统性审查和元分析 (PRISMA) 准则的首选报告项目进行了系统性文献审查.
- 对已识别的文献进行分析,以对PDF信息提取中的占主导地位的方法进行分类.
主要成果:
- 确定了三个主要的方法类别:基于规则的系统,统计学习模型和基于神经网络的方法.
- 关键的局限性包括基于规则的系统的不灵活性,基于学习的方法的数据稀缺性,以及大型语言模型中的幻觉等问题.
结论:
- 提出了一个由九个组成部分组成的概念框架,以提高PDF信息提取系统的准确性,适应性和可用性.
- 这个框架集成了各种模块,用于全面的PDF数据处理和知识检索.
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