Lexical Unit Mapping via Context Parameters
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Solution Overview
Problem
Conventional methods for aligning source and target texts in translation, such as heuristic rules and pre-constructed dictionaries, are not sufficiently precise and are resource-intensive, limiting the accuracy and efficiency of translation equivalence identification.
Innovation Solution
A computer-implemented method that analyzes the context-dependent and context-independent relationships between source and target lexical units to automatically map source lexical units to their translations without using dictionaries, by generating context parameters and comparing similarity values to determine translation equivalence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If heuristic rules are used for text alignment, then the method is simple to implement, but the alignment precision is insufficient
Solution Approach 1:
The patent transforms the alignment problem from using static heuristic rules to using dynamic context parameters (CDR values) that capture the statistical relationships between lexical units in their contextual environments. By changing from fixed rules to adaptive parameters based on co-occurrence statistics, the system achieves higher precision while maintaining computational feasibility.
Solution Approach 2:
The patent replaces the mechanical application of heuristic rules with a statistical model that computes context-dependent relation values. Instead of manually designed alignment rules, the system uses automated statistical analysis of lexical unit co-occurrences in parallel texts to determine alignment relationships, substituting mechanical rule-based processing with data-driven statistical inference.
2Measurement precision
If a pre-constructed translation dictionary is used, then mapping accuracy is improved, but the construction and maintenance is expensive and resource-intensive
Solution Approach 1:
The patent enables the system to automatically generate and update its own lexical unit mappings by analyzing parallel texts and computing context-dependent relation values. Instead of relying on externally constructed and maintained dictionaries, the system self-learns translation equivalences from the data, automatically adapting to new vocabulary and contexts without human intervention or external dictionary resources.
Solution Approach 2:
The patent creates virtual translation mappings through statistical analysis rather than copying from pre-existing dictionaries. By computing CDR values that represent contextual relationships, the system generates dynamic translation equivalents that are specific to the given parallel text, replacing the need to copy and store extensive pre-constructed dictionary data.
3Measurement precision
If context parameters are computed and compared, then mapping precision is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex task of lexical unit mapping into distinct computational stages: first computing CDR values for individual lexical units based on their co-occurrence patterns, then comparing these pre-computed parameters to identify equivalent units. This segmentation allows the system to handle computational complexity in manageable steps rather than attempting to solve the entire mapping problem simultaneously.
Solution Approach 2:
The patent performs preliminary computation of context-dependent relation parameters for all lexical units before executing the actual mapping process. By pre-computing and storing CDR values that capture contextual relationships, the system prepares the data structure in advance, reducing the computational burden during the final mapping phase where equivalences are determined by comparing these pre-computed parameters.
Data Source
AI summary
A method for mapping a source lexical unit in a first language with target lexical unit in a second language, the method comprising: acquiring a source digital text comprising a plurality of source sentences; parsing the plurality of source sentences into source lexical units; generating, for each of the one or more source lexical units a source context parameter; acquiring a target digital text comprising a plurality of target sentences; parsing the plurality of target sentences into target lexical units; generating for each one or more target lexical units, a target context parameter; selecting a first source lexical unit having a first source context parameter; comparing the first source context parameter with a plurality of target context parameters to determine a target context parameter having a lowest difference value; mapping the first source lexical unit with a first target lexical unit associated with the given target context parameter.