Short Phrase Language Identification via Search Context
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Solution Overview
Problem
Current machine translation systems require manual identification of the source language, which can be challenging for short phrases that may appear in multiple languages, leading to difficulties in achieving high-quality translations.
Innovation Solution
A method involving a computer system that submits a short phrase to a search engine to retrieve longer phrases containing the short phrase, which are then analyzed by a language identification engine to determine the most likely language, enabling accurate language identification and subsequent translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual language identification is used for short phrases, then the process is simple and direct, but the accuracy of language identification deteriorates because short phrases may appear in multiple languages
Solution Approach 1:
The system segments the language identification task into multiple stages: first submitting the short phrase to a search engine to retrieve longer contextual phrases, then analyzing these expanded phrases through a language identification engine. This segmentation allows the system to maintain operational simplicity while improving identification accuracy by examining phrases in broader context.
Solution Approach 2:
The system performs preliminary action by expanding the short phrase through search engine queries before actual language identification occurs. By pre-processing the input to generate longer contextual phrases, the system prepares more reliable data for the language identification engine, thereby improving accuracy without complicating the user interface.
2Productivity
If short phrases are used as input, then the user input is quick and convenient, but the reliability of language identification deteriorates due to ambiguity in short phrases
Solution Approach 1:
The search engine acts as an intermediary between the user's short phrase input and the language identification engine. It transforms the ambiguous short phrase into multiple longer contextual phrases that provide sufficient information for reliable language identification, thereby maintaining both user productivity and system reliability.
Solution Approach 2:
The system changes the parameter of phrase length by expanding short phrases into longer contextual phrases through search engine queries. This parameter transformation maintains the convenience of quick user input while improving the reliability of language identification by providing more linguistic context for analysis.
3Measurement precision
If longer phrases are analyzed instead of short phrases, then the accuracy of language identification improves, but the complexity of the system increases due to additional processing steps
Solution Approach 1:
The system achieves universality by making the search engine serve multiple functions: it acts as both a text expansion tool and a contextual provider for language identification. This multi-functionality allows the system to improve identification accuracy without adding dedicated complex components, as the search engine handles both phrase expansion and context provision.
Solution Approach 2:
The system employs self-service by using the search engine's inherent capabilities to automatically generate longer contextual phrases without requiring manual intervention. The search engine autonomously performs the expansion and contextualization tasks, reducing the need for additional complex processing components while improving language identification accuracy.
Data Source
AI summary
A computer receives a short phrase. The short phrase is transmitted in a query to a search engine. The computer receives one or more search results from the search engine in response to the query, and parses one or more longer phrases that include the short phrase from each of the one or more search results. The computer transmits the one or more longer phrases to a language identification engine for identification of the language of the one or more longer phrases, and receives from the language identification engine the language of each of the one or more the longer phrases. The computer then determines the most likely language of the short phrase, based at least in part on the language of each of the one or more the longer phrases.