Numerical Content Error Correction Using Type-Based Deep Learning
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Solution Overview
Problem
Current error correction technologies for numerical content in texts are limited in their ability to correct numerical errors involving reasoning calculations, particularly in formats and logical errors, leading to low recall rates and user experience issues.
Innovation Solution
A method and apparatus for error correction of numerical content in texts that determine the type of numerical content and apply specific correction methods based on these types, using deep learning models to identify and correct errors in formats and logical contexts, including normalization and contextual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general error correction methods are used for numerical content, then the correction process is simple, but the recall rate is low and logical errors cannot be corrected
Solution Approach 1:
The patent segments the error correction process into distinct modules: numerical content identification module, error type judgment module, and correction execution module. Each module handles specific aspects of the correction task, allowing the system to achieve high recall rates through specialized processing while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent changes the parameter of correction approach by introducing type-based differentiation. Instead of applying a uniform correction method, the system identifies different error types (format errors, logical errors, completeness errors) and applies corresponding correction strategies, thereby improving recall rate without proportionally increasing complexity.
2Reliability
If type-based error correction is implemented, then the recall rate improves, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple error types and their corresponding correction methods before the actual correction process. The system prepares correction strategies in advance for each error category (format, logical, completeness errors), enabling accurate and efficient correction without requiring complex real-time decision-making during execution.
Solution Approach 2:
The patent implements feedback mechanisms where the error type judgment module continuously identifies error categories and feeds this information to the correction execution module. This feedback loop ensures that the appropriate correction method is selected based on the identified error type, improving accuracy while keeping the system architecture straightforward through clear information flow.
3Reliability
If comprehensive error detection is performed, then more errors are corrected, but the processing time increases
Solution Approach 1:
The patent segments error detection into three distinct error type checks: format errors, logical errors, and completeness errors. Each segment focuses on specific aspects of numerical content validation, allowing comprehensive error detection to be performed through parallel or sequential specialized checks rather than a single complex analysis, thereby reducing overall processing time.
Solution Approach 2:
The patent changes the detection parameter by introducing error type classification. Instead of performing exhaustive analysis on all numerical content uniformly, the system detects and categorizes errors by type (format, logical, completeness), enabling targeted correction approaches that reduce processing time while maintaining comprehensive detection coverage.
Data Source
AI summary
This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.