Context-Aware Word Translation Using Database Segmentation
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Solution Overview
Problem
Traditional methods for translating or defining words in a sentence are inefficient as they fail to provide accurate and context-specific meanings due to multiple possible interpretations of words, making it difficult to understand the specific meaning of words within larger groups or phrases.
Innovation Solution
A method that queries databases to provide linguistically associated words within a sentence, using algorithms to analyze context, punctuation, and morphological structures to offer relevant translations and definitions, prioritizing results based on relevance and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional dictionary lookup approach is used to translate or define words, then the process is simple and fast, but the accuracy of context-specific meaning is poor due to multiple possible interpretations
Solution Approach 1:
The patent segments the translation process into multiple stages: first identifying candidate translations from a dictionary, then analyzing contextual clues from surrounding words, and finally selecting the most appropriate translation based on linguistic patterns and database queries. This segmentation allows the system to achieve high accuracy by processing information in discrete, manageable steps rather than attempting to evaluate all possible meanings simultaneously.
Solution Approach 2:
The patent introduces intermediary components including a database of linguistic expressions and algorithms that analyze contextual relationships between words. These intermediaries act as mediators between the raw dictionary definitions and the final translation selection, using contextual analysis to filter and rank candidate translations based on their appropriateness for the specific context.
2Loss of information
If multiple dictionary definitions are provided for a word with multiple meanings, then completeness of information is improved, but the difficulty of selecting the correct meaning increases
Solution Approach 1:
The patent implements feedback mechanisms where the system queries a database of linguistic expressions to gather information about how words are used in context. This feedback loop allows the system to evaluate candidate translations against actual usage patterns, automatically selecting the most appropriate meaning based on contextual evidence rather than requiring manual selection from multiple definitions.
Solution Approach 2:
The patent changes the parameter of translation selection from a static choice among predefined definitions to a dynamic process that adjusts based on contextual parameters. By analyzing surrounding words and linguistic patterns, the system adapts its selection criteria to the specific context, effectively changing the parameters used to evaluate and choose among multiple possible translations.
3Measurement precision
If contextual analysis of surrounding words is performed to determine the correct meaning, then translation accuracy is improved, but the response time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing linguistic expressions and contextual patterns in a database before they are needed for translation. When a translation request is made, the system can quickly query this pre-organized database rather than performing exhaustive analysis from scratch, significantly reducing response time while maintaining accurate contextual analysis.
Solution Approach 2:
The patent applies partial action by focusing contextual analysis only on the most relevant surrounding words and linguistic patterns rather than analyzing the entire text comprehensively. The system identifies and processes only the critical contextual elements necessary for disambiguation, performing sufficient analysis to achieve accuracy without the excessive computational burden of complete text analysis.
Data Source
AI summary
Disclosed is a method for providing at least one word linguistically associated with at least one searched word belonging to a set of words. After having queried (325) a first database of expressions to obtain a set of expressions including the at least one searched word and obtaining the set of expressions, a second database is queried (340), for each expression of at least an expression subset of the obtained set of expressions, to obtain at least one word linguistically associated with the at least one searched word and obtaining the at least one word linguistically associated with the at least one searched word. Next, at least one obtained word linguistically associated with the at least one searched word is selected (350).