Contextual Synonym Mapping for Cross-Language Job Matching
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Solution Overview
Problem
Existing language translation methods fail to account for multiple ways to express the same phrase in different languages, leading to incomplete matching of job descriptions and candidate profiles across languages.
Innovation Solution
A system that generates a contextual synonym database by translating phrases between languages and identifying similarities above a predetermined threshold, allowing for the recognition of multiple synonyms in different languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single direct translation is used to match job descriptions and candidate profiles across languages, then the matching process is simple and fast, but the matching accuracy is incomplete because multiple language variants are not captured
Solution Approach 1:
The patent segments the translation process into multiple components: (1) generating multiple translation variants for each phrase, (2) collecting contextual synonyms from job descriptions and candidate profiles, (3) creating a contextual synonym database that maps relationships between different language expressions. This segmentation allows the system to capture multiple language variants without requiring complete reanalysis of all text data each time a match is needed.
Solution Approach 2:
The patent performs preliminary actions by pre-building a contextual synonym database that stores pre-identified synonymous phrases across different languages. This database is constructed by analyzing job descriptions and candidate profiles beforehand, extracting phrases and their contextual synonyms, and storing these relationships for rapid retrieval during the matching process. This eliminates the need for real-time analysis of all possible language variants during candidate-job matching.
2Loss of information
If multiple translation variants are considered to improve matching completeness, then more contextual synonyms are captured, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis to build a contextual synonym database that pre-identifies and stores synonymous phrases across languages. By extracting phrases from job descriptions and candidate profiles beforehand and storing their contextual synonyms in a structured database, the system avoids the need to perform comprehensive language analysis during the actual matching process, thus reducing processing time while maintaining information completeness.
Solution Approach 2:
The patent creates a structured copy of language relationships in the form of a contextual synonym database. Instead of analyzing all possible language variants during matching, the system uses pre-created synonym mappings that replicate the relationships between different language expressions. This allows rapid retrieval and comparison of contextual synonyms without reprocessing the original text data.
3Adaptability or versatility
If a comprehensive contextual synonym database is built to capture all language variants, then matching relevance is improved, but the database size and storage requirements increase
Solution Approach 1:
The patent applies local quality by organizing the contextual synonym database in a structured manner that groups synonyms by language pairs and occupational categories. Each entry in the database contains specific phrase mappings relevant to particular job domains, allowing the system to retrieve only the locally relevant synonyms needed for a given matching task rather than processing the entire database. This structured organization reduces the effective data volume accessed during each matching operation.
Solution Approach 2:
The contextual synonym database is designed to serve multiple functions: it stores phrase translations, identifies contextual synonyms, and provides language adaptation across different occupational categories. By creating a universal database structure that can handle multiple language pairs and occupational domains simultaneously, the system achieves high language adaptability without proportionally increasing storage requirements, as the same database infrastructure serves diverse matching needs.
Data Source
AI summary
A subset of text associated with an occupational code and being in a language(s) different than a predetermined language is translated to a translated subset of text that is in the predetermined language. From a plurality of sets of natural language texts associated with a plurality of occupational codes that includes the occupational code, a set of natural language texts associated with the occupational code and being in the predetermined language is identified. For each natural language text from the set of natural language texts, a similarity between the translated subset of text and that natural language text is determined. In response to the similarity being greater than a predetermined threshold, the subset of text is identified as a contextual synonym of that natural language text.