Language Identification from Short Strings via Discriminative N-grams
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Solution Overview
Problem
Existing methods for language identification are ineffective when dealing with short strings, as they often require loading multiple dictionaries or relying on syntactic information and n-gram profiles, which are not practical or accurate for brief text samples.
Innovation Solution
A method that involves receiving user input as n-grams, determining their similarity to language representations based on occurrence frequencies, and identifying discriminative n-grams to create language-specific representations, allowing for accurate language identification even from short strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple dictionaries are loaded for language identification, then language identification accuracy is improved, but device memory usage and loading complexity increase
Solution Approach 1:
The patent segments the language identification task by extracting discriminative n-grams from multiple languages and creating separate language representations based on these segmented features. Instead of loading complete dictionaries, the system divides the problem into language-specific n-gram profiles that can be processed independently and stored more efficiently.
Solution Approach 2:
The patent extracts only the essential discriminative n-grams from language corpora to create compact language representations. By taking out only the most informative n-grams rather than entire dictionaries, the system achieves accurate language identification while significantly reducing memory requirements and loading complexity.
2Adaptability or versatility
If syntactic information and n-gram profiles are used for language identification, then language identification capability is improved, but effectiveness on short strings deteriorates
Solution Approach 1:
The patent applies local quality by creating language-specific n-gram representations that capture the unique characteristics of each language. By focusing on local discriminative patterns rather than global syntactic structures, the system achieves better performance on short strings where global patterns cannot be reliably established.
Solution Approach 2:
The patent changes the parameter of n-gram length and selection criteria to optimize for short strings. By adjusting which n-grams are considered discriminative and how they are weighted, the system adapts its language identification approach to work effectively with limited input data.
3Measurement precision
If discriminative n-grams are extracted for each language, then language representation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing discriminative n-grams for each language during an offline phase. This allows the system to have accurate language representations ready in advance, eliminating the need for complex real-time computation during language identification and thus maintaining high processing speed.
Data Source
AI summary
Systems and processes for language identification from short strings are provided. In accordance with one example, a method includes, at a first electronic device with one or more processors and memory, receiving user input including an n-gram and determining a similarity between a representation of the n-gram and a representation of a first language. The representation of the first language is based on an occurrence of each of a plurality of n-grams in the first language and an occurrence of each of the plurality of n-grams in a second language. The method further includes determining whether the similarity between the representation of the n-gram and the representation of the first language satisfies a threshold.