Foreign Title Standardization via N-gram Labeling
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Solution Overview
Problem
The lack of standardization in user-entered data on social networks, particularly in different languages, hinders effective searching, analyzing, and aggregating personal data, as existing methods are time-consuming and limited to manually mapped variants.
Innovation Solution
A system and process for generating a database of labeled foreign titles by translating and sanitizing titles, breaking them into n-grams, determining labels using a reference language database, and assigning inherited labels based on matching characteristics, enabling efficient mapping and aggregation of semantically equivalent titles across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data from different languages is treated independently with manual review and mapping, then data standardization can be achieved for each language, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary actions by automatically translating foreign language titles into the reference language and generating n-grams before manual review. This preprocessing step creates a structured foundation that significantly reduces the time required for subsequent manual mapping and standardization work.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a bridge between foreign language titles and the reference language database. This intermediary automatically translates titles, breaks them into n-grams, and matches them with existing labeled data, thereby mediating the complex task of multi-language standardization and reducing manual effort.
2Reliability
If manual mapping is used to create look-up tables for each language, then data variants can be mapped to representative terms, but the approach is limited to manually mapped variants and cannot handle unseen data
Solution Approach 1:
The system segments titles into n-grams (substrings of length n), allowing the matching process to work with individual components rather than complete titles. This segmentation enables the system to handle unseen and variant formulations by matching individual n-grams against the reference database, significantly improving adaptability while maintaining reliability through the structured matching process.
Solution Approach 2:
The patent applies partial matching by comparing n-grams rather than requiring complete title matches. This partial action approach allows the system to identify semantically equivalent titles even when they differ in wording or structure, thereby extending coverage to unseen variants while maintaining mapping accuracy through the n-gram comparison mechanism.
3Manufacturing precision
If complete manual review and mapping is performed for each language, then comprehensive data standardization can be achieved, but the complexity and resource requirements increase significantly
Solution Approach 1:
The system performs preliminary automated translation and n-gram generation before manual review, creating a structured intermediate representation. This preliminary action reduces the complexity of the subsequent manual mapping process by presenting pre-processed, organized data that requires less intensive manual intervention.
Solution Approach 2:
The patent introduces an intermediary automated processing layer that handles the complex tasks of translation, segmentation, and initial matching. This intermediary system absorbs much of the processing complexity, allowing the manual review stage to focus on higher-level validation and refinement, thereby reducing overall system complexity.
4Adaptability or versatility
If user-entered data is accepted in any format and language, then data collection is easy and comprehensive, but effective searching, analyzing, and aggregating becomes difficult
Solution Approach 1:
The system performs preliminary automated translation of foreign language titles into the reference language and generates n-grams before storage. This preliminary processing ensures that all data, regardless of original language or format, is converted into a standardized searchable format, maintaining data acceptance versatility while enabling efficient search and analysis operations.
Solution Approach 2:
By segmenting titles into n-grams and storing them in a standardized format with associated labels, the system enables flexible searching and analysis operations. The segmented structure allows users to search for specific terms or concepts across all languages while maintaining the ability to aggregate and analyze data effectively.
Data Source
AI summary
A system for generating a database of labeled foreign canonical titles includes an interface and a processor. The interface is to receive a title in a second language. The processor is to 1) store a set of n-grams in a first language in a first database; 2) sanitize the title into a sanitize title in the second language; 3) translate the sanitized title into a translated title in the first language; 4) break the translated title into n-grams; 5) determine labels for the n-grams using the first database; and 6) determine label to associate with the title.