NLP Labeling Using Text Features Across Variable Document Layouts
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Solution Overview
Problem
Existing natural language processing systems struggle to analyze documents with different layouts, as they primarily rely on document layout to determine hierarchical structures, neglecting variations in document format.
Innovation Solution
A natural language processing system that utilizes a weight array generated through learning, featuring a feature extraction unit and a label determination unit to analyze documents with different layouts by considering features and labels of character strings, including modification relationships within sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If document layout is used to determine hierarchical structure, then processing speed is improved, but adaptability to different document layouts deteriorates
Solution Approach 1:
The patent changes the parameters used for hierarchical structure determination from layout-based features to text-based features including modification relationships, part-of-speech tags, and semantic information. This allows the system to adapt to different document layouts while maintaining processing efficiency through learned models.
Solution Approach 2:
The patent replaces the mechanical layout-based analysis system with a machine learning-based natural language processing system. The learned model automatically adapts to different document types by learning from training data, eliminating the need for manual layout rule configuration and improving both adaptability and processing speed.
2Manufacturing precision
If layout analysis is performed to identify hierarchical structure, then structure determination accuracy is improved for fixed layouts, but versatility across different document types deteriorates
Solution Approach 1:
The patent creates a universal natural language processing system that can handle multiple document types and layouts through a single learned model. The model processes text sequences and modification relationships universally, adapting to different document types (invoices, receipts, contracts, etc.) without requiring separate layout analysis rules for each type.
Solution Approach 2:
The system changes from using layout-specific parameters to using universal text-based parameters such as modification relationships, part-of-speech tags, and semantic information. These parameters maintain high accuracy across different document types while enabling versatility through the learned model's ability to generalize from training data.
3Ease of manufacture
If rule-based layout analysis is used, then implementation simplicity is improved, but ability to handle unstructured documents deteriorates
Solution Approach 1:
The patent implements a self-learning system that automatically adapts to different document structures through machine learning. The learned model is trained on annotated data and automatically learns the relationships between text elements, modification patterns, and hierarchical structures, eliminating the need for manual rule creation and enabling handling of unstructured documents.
Solution Approach 2:
The patent replaces simple rule-based layout analysis with a machine learning-based system that processes text sequences and modification relationships. This substitution maintains implementation feasibility through standardized NLP pipelines while dramatically improving the ability to handle unstructured and varied document types through learned patterns.
Data Source
AI summary
A natural language processing system including: a weight array acquisition unit that acquires a weight array related to a weight for determining a label corresponding to a character string, the weight array being generated by learning based on at least one learning character string in which a feature and a label of at least one character string including at least one character are set; a feature extraction unit that extracts a feature corresponding to a target character string; and a label determination unit that determines the label of the target character string on the basis of the learned model generated by learning, the weight array, and the extracted at least one feature.