Hybrid Language Detection Model for Multilingual Network Management
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Solution Overview
Problem
Large enterprises with operations across multiple countries face challenges in managing networks that require communication in multiple human languages, necessitating effective language identification for proper handling and processing of written communications.
Innovation Solution
A system comprising a remote network management platform with a software application that categorizes characters by Unicode value ranges, calculates language likelihoods, and applies language models to determine the most probable language of a text string, sending the identified language to the client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a remote network management platform serves multiple countries with multiple languages, then the platform's adaptability and versatility improve, but the complexity of language identification and processing increases
Solution Approach 1:
The language detection system is segmented into multiple independent components: a symbol script family identification module that categorizes characters by Unicode ranges, and a language model application module that applies statistical models to determine language probability. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining multilingual support capability.
Solution Approach 2:
The patent introduces an intermediary classification step that first identifies the symbol script family of text characters before applying language-specific models. This intermediary layer acts as a mediator that narrows down the language detection scope, making the overall system more manageable by breaking the complex multilingual detection problem into smaller, more tractable sub-problems.
2Measurement precision
If language detection accuracy is improved through multiple language models and probability calculations, then measurement precision improves, but computational time and processing complexity increase
Solution Approach 1:
The system performs preliminary classification of text into symbol script families before applying detailed language models. This preliminary action pre-sorts the input text, allowing the system to quickly eliminate unlikely language candidates and focus computational resources on the most probable languages, thereby improving accuracy without proportionally increasing processing time.
Solution Approach 2:
The patent implements a two-stage detection process where the first stage (symbol script family identification) provides a quick, partial assessment that narrows down language possibilities. The second stage applies full language models only to the narrowed-down candidates. This partial action approach achieves high accuracy for the most likely languages without performing exhaustive analysis on all possible languages, thus reducing overall processing time.
Data Source
AI summary
An example embodiment may involve a software application executable on computing devices of a remote network management platform containing a computational instance associated with a managed network. A text string may be received, and characters of the string may be categorized among a plurality of symbol script families. A respective likelihood of the string corresponding to each family may be determined, and a respective probability of the string being in each language of each given family may also be determined. The respective probabilities for the languages of each given family may be weighted by the likelihoods of the given family, and then weighted sums of the probabilities for each language may be computed. The maximum of the weighted sums may correspond to the language of the text string. The respective probabilities may be determined according to hybrid N-gram and word language models for each family.