Translation Clustering Algorithm for Context-Free Dictionary Generation
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Solution Overview
Problem
The manual creation of bilingual dictionaries by human lexicographers is costly and time-consuming, and machine translation often relies on context, which can be limiting.
Innovation Solution
A computer-implemented technique that generates translation clusters by determining potential translations and synonyms for a source word in a target language using clustering algorithms, allowing for automatic and context-free organization of translations into clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of bilingual dictionaries by human lexicographers is used, then translation quality and accuracy are improved, but cost and time consumption increase
Solution Approach 1:
The patent uses machine translation systems to automatically generate translation clusters by copying and adapting existing translation data and synonym information, replacing the need for manual lexicographer work while maintaining translation quality through algorithmic processing of linguistic patterns
Solution Approach 2:
The system performs self-service by automatically determining potential translations, finding synonyms, generating clusters, and organizing results without human intervention, enabling the translation dictionary to be created and updated autonomously through computational processes
2Productivity
If machine translation relying on context is used, then translation speed is improved, but translation accuracy in specific contexts deteriorates
Solution Approach 1:
The patent segments translations into distinct clusters based on denotation and synonym relationships, allowing each cluster to represent a specific meaning context. This segmentation enables the system to provide multiple context-specific translation options rather than a single context-dependent translation, improving accuracy while maintaining speed
Solution Approach 2:
The system changes the parameter of translation representation from single context-dependent translations to multiple context-free translation clusters organized by denotation. This parameter change allows users to select the most appropriate translation for their specific context, improving accuracy without sacrificing the speed of machine translation
3Adaptability or versatility
If context-based translation is used, then translation relevance is improved, but complexity of processing increases
Solution Approach 1:
Instead of analyzing context to determine the appropriate translation, the patent inverts the approach by organizing translations into context-free clusters based on denotation and synonym relationships. This inversion allows users to apply their own contextual understanding when selecting from the clustered translations, reducing processing complexity while maintaining relevance
Data Source
AI summary
A computer-implemented technique can include receiving, at a server including one or more processors, a source word in a source language. The technique can include determining, at the server, one or more potential translations for the source word in a target language different than the source language. The technique can include determining, at the server, one or more synonyms for each of the one or more potential translations to obtain a plurality of potential translations. The technique can include determining, at the server, one or more translation clusters using the plurality of potential translations and a clustering algorithm. Each translation cluster can contain all of the plurality of potential translations that have a similar denotation and each of the plurality of translations that have a similar denotation can be included in a specific translation cluster. The technique can also include outputting, at the server, the one or more translation clusters.