Language Identification Using Weighted Multi-Model Confidence
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Solution Overview
Problem
Existing language identification systems face challenges in accurately identifying languages, particularly for short text strings, as they often rely on single models that perform poorly in such cases and may be compromised by named entities in different languages.
Innovation Solution
A system utilizing multiple language identification models and a gradient boosting machine (GBM) to weight confidence scores based on historical performance, improving accuracy by selecting the best prediction from these models and removing named entities to enhance prediction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single language identification model is used, then the system complexity is low, but the language identification accuracy deteriorates for short text strings
Solution Approach 1:
The patent combines multiple language identification models (including character n-gram, word n-gram, and neural network models) into a unified system that processes short text strings collectively. Each model contributes its strengths, and their predictions are aggregated through voting or confidence scoring mechanisms, achieving superior accuracy compared to any single model while maintaining manageable system complexity through modular architecture.
2Measurement precision
If multiple language identification models are applied, then the language identification accuracy improves, but the device complexity increases
Solution Approach 1:
The patent segments the language identification task into multiple specialized models, each optimized for specific aspects (character-level patterns, word-level patterns, contextual understanding). This segmentation allows each model to focus on particular features, improving overall accuracy while enabling independent training and evaluation of each component, thereby managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediary components including a named entity recognition module that removes or flags entities before language identification, and a confidence scoring mechanism that mediates between multiple model predictions. These intermediaries filter and prepare input data, reducing noise and improving the reliability of subsequent language identification without requiring direct complex interactions between all model components.
3Loss of information
If named entities are present in the text, then the text contains meaningful information, but the language identification accuracy deteriorates due to mixed languages
Solution Approach 1:
The patent extracts named entities from the input text using a dedicated named entity recognition module before passing the remaining text to language identification models. By removing or separately handling named entities that may contain foreign words or mixed languages, the system prevents these elements from confounding the language identification process, thereby maintaining accuracy for the primary language detection task while preserving entity information for separate processing.
Data Source
AI summary
Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.