String Similarity Processing Using Pronunciation and Character Patterns
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Solution Overview
Problem
Existing methods for determining the similarity between character strings, particularly those containing Chinese characters, face challenges in accuracy due to the complexity of Chinese character representation and pronunciation patterns.
Innovation Solution
A similarity processing method that acquires character strings, determines pronunciation pattern similarity and character pattern similarity, and calculates a comprehensive similarity by combining these dimensions, using edit distance algorithms and phonetic alphabets with tones, to improve accuracy in character string comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional edit distance algorithm is used to determine similarity between character strings, then the method is simple and fast, but the accuracy is insufficient especially for Chinese characters with complex pronunciation patterns
Solution Approach 1:
The patent segments the similarity determination process into three independent modules: character pattern similarity (based on edit distance), pronunciation pattern similarity (based on phonetic transcription), and comprehensive similarity (weighted combination). This segmentation allows each module to specialize in one aspect, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent transitions from single-dimension character comparison to multi-dimensional analysis by adding pronunciation pattern dimension alongside character pattern dimension. This dimensional expansion captures both visual similarity and phonetic similarity, significantly improving measurement precision for Chinese characters where pronunciation carries semantic information.
2Measurement precision
If only character pattern is considered for similarity determination, then the processing is simple, but homophones and near-homophones cannot be accurately distinguished
Solution Approach 1:
The patent separates character pattern analysis and pronunciation pattern analysis into distinct processing streams. The character pattern module handles visual similarity using edit distance, while the pronunciation module handles phonetic similarity using phonetic transcription and specialized distance calculation. This segmentation enables accurate homophone discrimination without overwhelming complexity.
Solution Approach 2:
The patent introduces phonetic transcription as an intermediary representation that bridges character form and pronunciation. By converting characters to their phonetic equivalents and calculating distance between these representations, the system can accurately distinguish homophones and near-homophones while maintaining a systematic processing framework.
3Measurement precision
If multiple dimensions including pronunciation and character patterns are considered, then the similarity determination accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides the comprehensive similarity calculation into separable components: character pattern similarity, pronunciation pattern similarity, and their weighted combination. Each component can be computed independently and cached, allowing efficient reuse in multiple comparison scenarios and reducing redundant computation.
Solution Approach 2:
The patent introduces weighted parameters to balance the contribution of character pattern similarity and pronunciation pattern similarity to the comprehensive similarity score. By adjusting these weights based on specific application requirements, the system can optimize processing efficiency while maintaining high accuracy, allowing flexible adaptation to different performance needs.
Data Source
AI summary
The present application discloses a similarity processing method, an apparatus, a server and a storage medium, and relates to the fields of information processing and natural language processing. The specific implementation solution is as follows: acquiring a first character string and a second character string; determining a pronunciation pattern similarity and a character pattern similarity between the first character string and the second character string; and determining a comprehensive similarity between the first character string and the second character string, based on the pronunciation pattern similarity and the character pattern similarity.