NLP Training Data Generation from Video Analysis
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Solution Overview
Problem
The generation of suitable training data for Natural Language Processing (NLP) models is costly and time-consuming, especially for less common languages or dialects, as traditional methods rely on manual labeling and often lack sufficient data, limiting the applicability of NLP systems.
Innovation Solution
A system that generates training data from video content by analyzing subtitles, video frames, metadata, and audio levels to extract emotion, language style, and brand perception data, which can be used to train machine learning models for NLP techniques, leveraging freely available online video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to generate training data, then the quality of training data can be ensured, but the cost and time required increase significantly
Solution Approach 1:
The patent replaces manual mechanical labeling processes with automated machine learning systems. Specifically, it uses pseudo-labeling algorithms and self-training mechanisms to automatically generate training data without human intervention, thereby eliminating the time-consuming manual labeling process while maintaining data quality through algorithmic precision
Solution Approach 2:
The system enables training data to be generated autonomously by the machine learning model itself through self-training and pseudo-labeling. The model generates its own training data by processing unlabeled data, assigning pseudo-labels, and iteratively improving itself, thus serving its own data generation needs without external manual input
2Measurement precision
If manual labeling is used for training data generation, then data accuracy can be maintained, but the cost increases
Solution Approach 1:
The patent substitutes expensive manual labeling operations with automated machine learning systems. By using pseudo-labeling and self-training algorithms, the system eliminates the need for human annotators, thereby dramatically reducing data generation costs while maintaining accuracy through algorithmic consistency and scalability
Solution Approach 2:
The system changes the operational parameters of data generation from manual human processes to automated computational processes. This parameter change transforms the cost structure from labor-intensive to computation-intensive, leveraging the scalability and low marginal cost of automated ML systems to produce accurate training data at lower costs
3Device complexity
If traditional training data generation methods are used, then process simplicity is maintained, but the extent of automation is limited
Solution Approach 1:
The patent implements self-service automation where the machine learning system automatically generates its own training data through pseudo-labeling and self-training mechanisms. This automation handles the entire data generation pipeline autonomously, from processing unlabeled data to generating labeled training examples, significantly increasing the extent of automation while maintaining a relatively simple overall process architecture
Data Source
AI summary
A training data system enables the generation of training data based on video content received from one or more outside video sources. For example, the generated training data can include a transcript of a word or phrase alongside emotion, language style, and brand perception data associated with that word or phrase. To generate the training data from a video, the subtitles, video frame, metadata, and audio levels of the video can be analyzed by the training data system. The generated training data (potentially from a plurality of videos) can then be grouped into a set of training data and used to train machine learning modules for Natural Language Processing (NLP) techniques.