Text Error Correction via Multi-Type Non-Word Segmentation
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Solution Overview
Problem
Current text error correction methods are limited in accuracy as they can only check and correct single types of characters, leading to low precision in error detection and correction.
Innovation Solution
A text processing method that detects and filters multiple types of characters by categorizing non-words into specific types and applying corresponding correction modes, such as adding spaces for compound incorrect non-words or modifying non-letter characters, to improve error correction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single type of character is checked and corrected using dictionary or language model methods, then the correction process is simple, but the accuracy of error correction is low
Solution Approach 1:
The patent segments non-word errors into four distinct types (all-uppercase-letter non-words, compound incorrect non-words, non-words with non-letter characters, and other non-words), applying different correction strategies to each type. This segmentation enables more accurate error correction by treating different error patterns differently, thereby improving measurement precision without requiring a completely complex system overhaul.
Solution Approach 2:
The patent applies local quality by using different correction approaches for different types of non-words. For example, compound incorrect non-words are handled by inserting spaces, while all-uppercase-letter non-words are converted to lowercase. This localized treatment of different error types improves correction accuracy while keeping each specific correction method relatively simple.
2Measurement precision
If multiple types of characters are detected and filtered with different correction modes, then the accuracy of text error correction is improved, but the complexity of the correction process increases
Solution Approach 1:
The correction process is segmented into distinct stages: error detection, error classification into four types, and type-specific correction application. This segmentation makes the complex multi-type correction process more manageable and easier to operate, as each stage has a clear, focused function rather than requiring simultaneous handling of all error types.
Solution Approach 2:
The patent changes the parameter of error classification by introducing four distinct error types with specific characteristics. This parameter change enables the system to automatically select appropriate correction modes based on the detected error type, improving accuracy while maintaining ease of operation through automated parameter-based decision making.
3Reliability
If all non-words are corrected using the same method, then the correction process is straightforward, but the robustness of the error correction method is reduced
Solution Approach 1:
The patent segments the correction method into four distinct pathways based on error type, with each pathway optimized for its specific error category. This segmentation enhances robustness by ensuring that each error type receives the most appropriate correction treatment, making the overall system more reliable without requiring an overly complex unified approach.
Solution Approach 2:
The patent achieves universality by creating a multi-functional correction system that can handle four different types of non-word errors through a single unified framework. The system universally applies error detection followed by type-specific correction, making it robust across diverse error scenarios while maintaining a relatively simple overall structure.
Data Source
AI summary
This application discloses a text processing method and apparatus in the natural language processing field in the artificial intelligence field. The method includes: obtaining a to-be-processed text; performing error detection processing on the to-be-processed text to obtain a non-word in the to-be-processed text; and if the non-word in the to-be-processed text belongs to a first type of non-word, skipping correcting the non-word in the to-be-processed text; or if the non-word in the to-be-processed text belongs to a second type of non-word, a third type of non-word, or a fourth type of non-word, selecting a correction mode matching the type of the non-word in the to-be-processed text to correct the non-word in the to-be-processed text and obtain a correction result of the non-word in the to-be-processed text. According to the technical solutions of this application, a plurality of types of character strings can be detected and filtered.