Localized Keyword Translation Using Corpus Frequency Analysis
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Solution Overview
Problem
Existing keyword translation methods, such as traditional machine translation and human translation, often fail to accurately convey the localized meaning of keywords across languages, as they rely on dictionaries or general language knowledge, lacking context and frequency data specific to regional usage.
Innovation Solution
A method and system for localized keyword translation that uses corpora associated with the target language, such as search query logs and social network content, to determine the frequency and context of candidate keywords, selecting the most appropriate translations based on their occurrence and relevance to the target audience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine translation or human translation is used to translate keywords, then translation can be performed, but the translation accuracy and contextual relevance are insufficient because they rely on dictionaries or general language knowledge without frequency data
Solution Approach 1:
The system pre-processes and stores frequency data from corpora (search logs, social media, news articles) before translation is needed. This preliminary action builds a foundation of contextual usage patterns that enables more accurate translations without adding complexity during the actual translation process
Solution Approach 2:
The patent introduces an intermediary component that bridges traditional translation methods and contextual accuracy. This intermediary uses frequency data from multiple corpora to select the most appropriate translation from candidate translations, acting as a mediator between generic translation outputs and contextually accurate results
2Measurement precision
If corpora analysis is used to determine keyword translation frequency and context, then translation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of corpora to extract and store frequency data in advance. By pre-computing translation frequencies from search logs, social media, and news articles before translation requests arrive, the system avoids time-consuming analysis during actual translation operations
Solution Approach 2:
The system dynamically adjusts its approach based on available resources and requirements. It can use pre-computed frequency data for quick translations or perform additional real-time corpus analysis when higher accuracy is needed and resources are available, making the processing time flexible rather than fixed
3Adaptability or versatility
If multiple corpora are analyzed to identify frequently occurring keywords, then contextual relevance and localized meaning improve, but the complexity of data processing and selection increases
Solution Approach 1:
The system segments the complex task of analyzing multiple corpora into distinct, manageable components. Each corpus (search logs, social media, news articles) is processed separately to extract frequency data, which are then combined and evaluated. This segmentation reduces overall complexity by breaking down the monolithic processing task into modular steps
Data Source
AI summary
In one implementation, a method includes receiving a request for translation of one or more first keywords from a source language to a target language; and translating, using a machine translation process, the first keywords from the source language into a plurality of second keywords in the target language. The method can also include determining, by a computer system, frequencies with which each of the second keywords occur in a corpus associated with the target language. The method can further include selecting, by the computer system, a subset of the second keywords to use in the target language based on the determined frequencies of occurrence.