Tabular Data Structure Extraction via Rectangle Mining
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Solution Overview
Problem
Financial tables with complex semantic structures pose challenges for automated comprehension due to the lack of standardized presentation standards and visual cues, making it difficult for machines to understand the relationships between data cells and header cells.
Innovation Solution
A computer-implemented method that processes tabular data by identifying regions, matching them to semantically related header cells, and generating tuples containing semantic information, using a rectangle mining framework and the REMINE algorithm to extract hierarchical relationships and semantic hierarchies, thereby mapping data cells to relevant header cells for improved understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If financial tables use complex semantic structures to present denser information, then information density is improved, but automated comprehension becomes more difficult
Solution Approach 1:
The patent segments the table comprehension task into multiple processing stages: identifying candidate header-cell pairs, computing semantic similarity scores, filtering pairs based on thresholds, and handling disagreements through voting mechanisms. This segmentation transforms the complex automated comprehension problem into manageable sub-tasks that can be processed systematically.
Solution Approach 2:
The patent introduces semantic similarity scoring as an intermediary mechanism between the table structure and the comprehension process. By computing similarity scores between header cells and data cells using vector embeddings, the system creates an intermediate representation that bridges the gap between complex tabular data and machine-understandable semantics.
2Ease of operation
If financial tables are designed for human consumption without standardized presentation standards, then ease of human interpretation is improved, but machine understanding deteriorates
Solution Approach 1:
The patent transforms the table data into vector embeddings, changing the parameter representation from raw text to numerical vectors that capture semantic meaning. This parameter transformation enables machines to understand semantic relationships without requiring standardized presentation formats, maintaining human interpretability while improving machine comprehension.
Solution Approach 2:
The semantic similarity computation mechanism serves multiple functions: it identifies header-cell pairs, determines semantic relationships, and provides a basis for filtering and voting. This universal approach handles various table formats and structures without requiring format-specific processing, making the system both human-friendly and machine-comprehensible.
3Device complexity
If pair-wise classification is used for simple table structures, then processing simplicity is improved, but accuracy in handling complex semantic structures deteriorates
Solution Approach 1:
The patent merges multiple pair-wise classification results through a voting mechanism. By combining results from different semantic similarity computations and applying threshold-based filtering, the system achieves higher accuracy in identifying correct header-cell pairs while maintaining the simplicity of pair-wise processing for individual comparisons.
Solution Approach 2:
The voting mechanism provides feedback by aggregating multiple classification results and identifying the most consistent header-cell pairs. This feedback loop allows the system to refine its selections and improve accuracy by considering multiple perspectives on the semantic relationships within the table.
Data Source
AI summary
Methods, systems, and computer program products for extracting structure and semantics from tabular data are provided herein. A computer-implemented method includes processing tabular data comprising data cells and header cells, wherein the processing includes: identifying one or more regions within the tabular data, wherein each of the regions comprises one or more of the data cells; matching some of the regions to one or more of the header cells, wherein the matched header cells are semantically related to the data cells inside the matched region; and generating, based on the matching, an output describing semantic relationships between the data cells and the header cells. The method also includes creating, for each data cell, a tuple comprising semantic information contained within one or more of the header cells that pertains to the data cell.