Language Detection Using Pre-compressed Dictionary Documents
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Solution Overview
Problem
Existing language determination services are resource-intensive and inefficient, leading to slow response times and poor accuracy due to their reliance on recompressing text data for each language detection, which results in a detrimental user experience.
Innovation Solution
The implementation of quick language detection using already compressed dictionary documents and machine-learning techniques to determine language based on reference scores, avoiding the need for multiple compression iterations and reducing the burden on computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language determination services use traditional text compression methods for each language detection, then comprehensive language analysis can be performed, but response times become slow and computing resources are consumed excessively
Solution Approach 1:
The patent pre-compresses language documents into dictionary documents before language detection is needed. These pre-compressed dictionary documents are stored and reused for multiple language detection operations, eliminating the need to perform compression repeatedly. This preliminary action significantly reduces response time while maintaining detection accuracy.
Solution Approach 2:
The system maintains compression states of dictionary documents that represent the current position in the compressed data. This allows the system to resume compression from where it left off rather than starting over, further optimizing performance for sequential language detection tasks.
2Measurement precision
If traditional language determination services process text data through full compression algorithms, then accurate language identification can be achieved, but computing resources such as processors and memory are used inefficiently
Solution Approach 1:
The patent creates compressed copies of language documents (dictionary documents) that can be reused multiple times. Instead of performing full compression algorithms repeatedly on the same language data, the system uses these pre-compressed copies, dramatically reducing processor and memory usage while maintaining identification accuracy.
Solution Approach 2:
The system discards the original uncompressed language documents after they have been compressed into dictionary documents. The compressed form is retained and reused, eliminating the need to store and process both original and compressed versions simultaneously, thus reducing memory consumption.
3Adaptability or versatility
If language determination services are unrelated to specific contexts, then they can be universally applied to different languages, but accuracy of the identified language decreases
Solution Approach 1:
The patent creates separate dictionary documents for different languages, each optimized for its specific linguistic characteristics. Instead of using a single generic compression model for all languages, the system maintains language-specific compressed representations, allowing accurate detection while covering multiple languages through the collection of specialized dictionary documents.
Data Source
AI summary
Implementations are directed to receiving text data including a string of characters, processing the text data to determine a set of reference scores including two or more reference scores, each reference score being associated with a respective language, and being determined based on the text data and a dictionary document provided for the respective language, each dictionary document including a compression of a language document provided in the respective language, selectively determining a language of the text data based on the set of reference scores, and providing language data representative of the language as output.