Multi-Stack Parser for Financial Table Structure Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for extracting mathematical structures from financial tables, especially those beyond balance sheets, face challenges such as handling sparse tables, zero values, and complex hierarchical structures, often resulting in inefficiencies and inaccuracies due to their reliance on single-stack parsers and specific keyword-based approaches.
Innovation Solution
The method employs a multi-stack parser system with a combination of a normal stack for reduce operations and a flat stack for dubious decisions, allowing for efficient backtracking and handling of complex tables by storing all decisions and analyzing rows, thereby improving the detection of mathematical structures in financial documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-stack parser is used for extracting mathematical structures from financial tables, then the device complexity is reduced, but the accuracy and reliability of handling complex hierarchical structures and sparse tables deteriorates
Solution Approach 1:
The parser is segmented into multiple independent stacks (normal stack and flat stack) that operate in parallel. The normal stack handles confident reduce operations while the flat stack preserves dubious decisions for later analysis. This segmentation allows the system to maintain low individual stack complexity while achieving high overall reliability through distributed decision-making.
Solution Approach 2:
The system changes the operational parameters of the parser by introducing a confidence threshold mechanism. Decisions are categorized as either confident (processed immediately by normal stack) or dubious (preserved in flat stack for later review). This parameter-based classification transforms the parser from a single rigid structure to a flexible multi-mode system that adapts its processing behavior based on decision confidence levels.
2Ease of operation
If keyword-based approaches are used for detecting mathematical structures, then the ease of operation is improved, but the adaptability to handle diverse financial table types beyond balance sheets deteriorates
Solution Approach 1:
The multi-stack parser is designed as a universal system that can handle multiple types of financial tables (balance sheets, income statements, cash flow statements, and sparse tables) using the same core mechanism. Instead of requiring different keyword-based approaches for different table types, the system uses a unified parser that adapts to various table structures through its flat stack backtracking capability, making it versatile across all financial document types.
3Productivity
If traditional parsing methods are used for sparse tables with zero values, then the processing speed is maintained, but the accuracy of detecting mathematical structures deteriorates
Solution Approach 1:
The system performs preliminary action by preserving all dubious reduce decisions in the flat stack before finalizing the parse tree. When processing sparse tables with zero values, this allows the system to later review and correct decisions that might have been incorrectly made during initial parsing, thereby improving accuracy without significantly impacting processing speed since the review only occurs for marked dubious decisions.
Data Source
AI summary
Disclosed is a method and system for extracting a mathematical structure associated with a financial table. According to an exemplary embodiment, the method uses a LR-(Left-to-Right) parser reducing stack and a LR-parser nonreducing stack to generate a final reducing stack representative of the mathematical structure.