Video Language Labeling Using Reference-Language Confidence Checks
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Solution Overview
Problem
Existing language labeling methods for user-generated video content face challenges due to writing irregularities such as spelling errors, transliteration, and code-switching, requiring manual labeling by proficient technicians and are inefficient for minority languages, especially when training classifiers for hundreds of languages.
Innovation Solution
A language labeling method using a machine learning-based classifier, pre-trained with supervised learning, that collects and filters video information, divides it into target and reference information, and checks confidence levels using reference languages to improve accuracy, incorporating semi-supervised learning for iterative updates with incremental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for language classification, then labeling accuracy can be improved, but labeling efficiency deteriorates and technical threshold increases
Solution Approach 1:
The system enables automatic language labeling through the language classifier that processes video information autonomously without requiring manual intervention for each video, thereby improving efficiency while maintaining accuracy through the self-learning capability of the classifier
Solution Approach 2:
The patent replaces the manual mechanical labeling process with an automated language classifier system that uses machine learning algorithms to automatically identify and label languages in video content, eliminating the need for human technicians to perform repetitive labeling tasks
2Measurement precision
If more video information is collected for language identification, then language prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments video information into multiple distinct types (video description, title, comments, tags) and processes them through a unified language classifier, allowing the system to leverage diverse information sources while maintaining manageable processing complexity through modular information categorization
Solution Approach 2:
The language classifier is designed as a universal system that can process multiple types of video information (descriptions, titles, comments, tags) simultaneously, enabling the system to improve accuracy by collecting diverse information without proportionally increasing processing complexity
Data Source
AI summary
Provided is a language labeling method, including: determining a language classifier; collecting a plurality of pieces of information correlated to video data, and determining the plurality of pieces of information as a plurality of pieces of video information; dividing the plurality of pieces of video information into target information and reference information; identifying languages to which the plurality of pieces of video information is attributed by inputting the plurality of pieces of video information into the language classifier; and checking a confidence level of a target language with an aid of reference languages, wherein the target language is a language to which the target information is attributed and the reference languages are a plurality of languages to which the reference information is attributed.