Graph Representation Learning for Key-Value Extraction
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Solution Overview
Problem
Current feature extraction methods in document processing are optimized for specific types of documents and require segmentation, limiting their versatility in extracting key-value pairs across various document types.
Innovation Solution
A universal technique that encodes documents to identify different types of keys and values, using representation learning to analyze the encoded document and learn the association between keys and values, enabling the extraction of key-value pairs without segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation approach is used for feature extraction, then extraction accuracy for specific document types is improved, but versatility across different document types deteriorates
Solution Approach 1:
The patent applies universality by creating a single feature extraction system that can handle multiple document types (text, image, audio, video) without requiring separate segmentation approaches for each type. The system uses a unified deep learning model that automatically adapts to different document characteristics, eliminating the need for type-specific segmentation while maintaining high extraction accuracy across all document types
2Reliability
If segmentation approach is used for feature extraction, then extraction reliability for specific document types is improved, but processing complexity deteriorates
Solution Approach 1:
The patent merges multiple segmentation and extraction operations into a single integrated deep learning model. Instead of separately segmenting documents and then extracting features using different methods for each segment type, the system combines these operations into one unified process that simultaneously performs segmentation and extraction using neural network layers, reducing overall processing complexity while maintaining reliability
3Ease of operation
If traditional extraction methods are used, then ease of operation for specific document types is improved, but automation capability deteriorates
Solution Approach 1:
The patent implements self-service automation through the deep learning model that automatically learns to extract features from different document types without requiring manual programming of extraction rules for each document type. The system self-adapts to new document formats through training data, eliminating the need for manual configuration while maintaining ease of operation across diverse document types
Data Source
AI summary
Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.