Abbreviation Detection and Mapping in Translation Pipelines
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Solution Overview
Problem
Existing language processing systems face difficulties in accurately recognizing and handling abbreviations, particularly due to their prevalence and evolution within local environments, which can lead to inconsistent and low-quality linguistic data, limiting the reuse and performance of translation processes.
Innovation Solution
A system utilizing human language translation capabilities and non-exact matching techniques, including a ruleset that considers factors like camel case and consecutive consonants, to map abbreviation candidates to their original full terms, leveraging existing resources to reduce effort and expense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language processing methods are used to handle abbreviations, then the system can process text, but the accuracy of recognizing and mapping abbreviations to full terms deteriorates due to their prevalence and evolution in local environments
Solution Approach 1:
The patent introduces an intermediary component - an abbreviation detection and mapping system that acts as a mediator between the input text and the translation service. This intermediary identifies abbreviations, maps them to full terms using a database and similarity matching, and then passes the enriched text to the translation service, thereby improving accuracy without requiring the translation service itself to adapt to local abbreviations.
Solution Approach 2:
The system performs preliminary action by detecting and expanding abbreviations before the text is sent to the translation service. By pre-processing the text to replace abbreviations with their full forms using a database of known abbreviations and similarity matching, the system ensures that the translation service receives complete, unabbreviated text, improving translation accuracy without requiring the service to handle abbreviations.
2Measurement precision
If manual methods are used to identify and map abbreviations to full terms, then accuracy can be maintained, but the effort and expense increase significantly
Solution Approach 1:
The system implements self-service by automatically detecting abbreviations, querying the abbreviation database, performing similarity matching, and mapping abbreviations to full terms without requiring manual intervention. The automated pipeline includes abbreviation detection based on linguistic patterns, database querying, similarity scoring, and automatic selection of the best match, eliminating the need for manual abbreviation identification while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational processes. Instead of manually identifying and mapping abbreviations, the system uses automated abbreviation detection algorithms, database querying, and computational similarity matching (e.g., Levenshtein distance) to perform the same function with greater efficiency and lower cost, while maintaining or improving accuracy.
3Productivity
If abbreviation data is not properly handled, then processing is simpler, but the quality and reusability of linguistic data deteriorates
Solution Approach 1:
The system performs preliminary action by expanding abbreviations to their full forms before the text enters the translation pipeline. This pre-processing step ensures that the linguistic data stored in the database and processed by the translation service is complete and consistent, improving both the quality and reusability of the data without compromising processing efficiency, as the expansion happens automatically in the background.
Data Source
AI summary
Translation capability for language processing determines an existence of an abbreviation, followed by non-exact matching to map the abbreviation to the original full term. A received string in a source language is provided as input to a translation service. Translation proposals in a different target language are received back. A ruleset (considering factors, e.g., camel case format, the presence of a concluding period, and/or consecutive consonants) is applied to generate abbreviation candidates from the translation proposals. Non-exact matching (referencing e.g., a comparison metric) may then be used to map the abbreviation candidates to text strings of their original full terms. A mapping of the abbreviation to the text string of the original full term is stored in a translation database comprising linguistic data. Embodiments leverage existing resources (e.g., translation service, non-exact matching) to reduce effort and expense of accurately identifying abbreviations and then mapping them to their full original terms.