Simplified Traditional Chinese Character Conversion Priority Model
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Solution Overview
Problem
Current automatic conversion tools for Simplified and Traditional Chinese characters often fall short in precision, especially in one-to-many cases, leading to errors that require costly manual rectification in high-end document processing, and rely solely on limited data resources which can result in inconsistent conversion quality.
Innovation Solution
A method and system that utilizes a priority-based multi-data resource management model, combining preference and authority priorities, along with a revised N-Gram statistical model and reverse maximum matching algorithm, to enhance the accuracy and efficiency of character conversions by selecting the most pertinent data resources and handling regional variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic conversion tools are used for Simplified-Traditional Chinese character conversion, then conversion efficiency is improved, but conversion precision deteriorates due to one-to-many cases and limited data resources
Solution Approach 1:
The patent segments the conversion process into multiple stages: candidate generation from multiple data resources, filtering based on priority levels, and final selection. This multi-stage segmentation allows the system to maintain high efficiency while improving precision by systematically handling one-to-many cases through structured processing steps
Solution Approach 2:
The patent merges multiple data resources (dictionaries, corpora, linguistic databases) into a unified conversion system with hierarchical priority levels. By combining diverse data sources and integrating them with statistical models and rule-based approaches, the system achieves both high efficiency and precision in character conversion
2Measurement precision
If multiple data resources are used to improve conversion accuracy, then conversion precision is improved, but system complexity increases
Solution Approach 1:
The patent segments multiple data resources into hierarchical priority levels (first priority, second priority, etc.), organizing them in a structured manner. This segmentation reduces system complexity by providing a clear hierarchy for resource selection, making the multi-resource system more manageable and easier to implement
Solution Approach 2:
The patent applies different quality standards and processing methods to different data resources based on their priority levels and characteristics. High-priority resources receive more rigorous validation and are used for critical conversions, while lower-priority resources serve supplementary roles, optimizing the overall system performance without uniform complexity
3Ease of operation
If conventional conversion methods are used, then ease of operation is maintained, but conversion quality deteriorates in high-end document processing
Solution Approach 1:
The patent implements self-service mechanisms where the conversion system automatically selects appropriate data resources and applies suitable conversion methods based on the input context and character type. This automation maintains ease of operation while improving conversion quality, as the system autonomously handles the complexity of selecting from multiple data resources without requiring user intervention
Data Source
AI summary
Method, system and medium for character converting between different regional versions of a language especially between Simplified Chinese and Traditional Chinese are provided. The method comprises finding for the source character a target character, for example by finding the target character in a desired data resource from the plurality of data resources which are managed by a multiple category management model with regard to data resources' priorities. The method may offer users greater flexibility in choosing the data resources most appropriate to their conversion purposes to increase the efficiency and accuracy of the conversion, and meanwhile does not have to search all the data resources before offering a conversion candidate in each operation, thereby shortening the running time of conversion.