代表学习方法用于理解结构化文档
Akkshita Trivedi1, Sandeep Khanna2, Santanu Chaudhury3
1Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India. trivedi.2@iitj.ac.in.
文档关系实体嵌入学习器 (D-REEL) 通过模拟文本,图形和表格之间的语义关系来提高文档理解. 这种新的框架可以在复杂的布局中提高相关性准确性和提取性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 当前的文档理解方法与复杂的布局和元素之间的逻辑连接作斗争.
- 现有的方法未能在各种文档结构中捕捉到深层次的语义关系.
研究的目的:
- 介绍一个新的表示学习框架,文档关系实体嵌入学习者 (D-REEL),以建模文档中的复杂语义关系.
- 通过准确评估文档字段之间的语义相关性来提高文档的理解,无论它们的空间布局如何.
主要方法:
- D-REEL生成提取候选项,并为每个候选项学习密集向量表示 (嵌入).
- 比较嵌入,以评估语义相关性和确定文章关系.
- 将空间信息与特定领域的模式相结合,用于精确的提取和相关性评分.
- 建议使用位置无神论定位的语义结构一致性 (SSC) 度量.
主要成果:
- 在公开数据集上显示的相关性准确性和提取性能显著改善.
- 在相关性准确性方面实现了2-3%的平均mAP增量.
- 在PRIMA数据集中获得了近10%的SSC改进,表明有效的关系测量.
结论:
- 对于理解复杂的文档布局和语义关系,D-REEL提供了一个强大的解决方案.
- 该框架允许精确的提取和可靠的相关性评分,即使是不规则的文档结构.
- 拟议的SSC指标有效量化了文档中的语义结构一致性.
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