Concept Vector Mediator for Automated Training Data Labeling
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Solution Overview
Problem
Existing methods for training machine learning systems to classify natural language passages require extensive manual labeling of training examples, which is time-consuming and expensive, making it impractical to generate sufficient labeled data for multiple tasks, leading to exponential growth in the number of required examples as the number of tasks increases.
Innovation Solution
A system and method that automatically labels training data examples using conceptual descriptions, where an electronic processor generates unlabeled examples, determines associated concepts, creates weak annotators, applies them to examples, and outputs categories based on probabilistic distributions to reduce manual effort and generate labeled data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to generate training examples, then labeling accuracy can be ensured, but time consumption and cost increase exponentially with the number of tasks
Solution Approach 1:
The patent introduces concept vectors as an intermediary between the training examples and category labels. These concept vectors serve as a mediator that automatically captures semantic relationships, eliminating the need for manual labeling while maintaining accuracy through vector-based semantic matching.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system using concept vectors and similarity calculations. This substitution eliminates human intervention in the labeling process, dramatically reducing time consumption while maintaining consistent labeling quality.
2Measurement precision
If manual labeling methods are used to generate training examples, then category accuracy can be maintained, but cost increases exponentially with the number of tasks
Solution Approach 1:
The patent introduces concept vectors as an intermediary between the training examples and category labels. These concept vectors serve as a mediator that automatically captures semantic relationships, eliminating the need for manual labeling while maintaining accuracy through vector-based semantic matching.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system using concept vectors and similarity calculations. This substitution eliminates human intervention in the labeling process, dramatically reducing time consumption while maintaining consistent labeling quality.
3Quantity of substance
If extensive manual labeling is performed to cover multiple tasks, then sufficient training data can be obtained, but the complexity of the labeling process increases
Solution Approach 1:
The patent creates a universal concept vector framework that can handle multiple different classification tasks simultaneously. The same concept vector generation and matching process works across diverse tasks (sports classification, novel genre classification, etc.), eliminating the need for separate labeling processes for each task.
Solution Approach 2:
The patent introduces concept vectors as an intermediary between the training examples and category labels. These concept vectors serve as a mediator that automatically captures semantic relationships, eliminating the need for manual labeling while maintaining accuracy through vector-based semantic matching.
4Productivity
If automated labeling methods are used, then time consumption and cost are reduced, but labeling accuracy may deteriorate
Solution Approach 1:
The patent introduces concept vectors as an intermediary between the training examples and category labels. These concept vectors serve as a mediator that automatically captures semantic relationships, eliminating the need for manual labeling while maintaining accuracy through vector-based semantic matching.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system using concept vectors and similarity calculations. This substitution eliminates human intervention in the labeling process, dramatically reducing time consumption while maintaining consistent labeling quality.
Data Source
AI summary
A system for automatically labeling data using conceptual descriptions. In one example, the system includes an electronic processor configured to generate unlabeled training data examples from one or more natural language documents and, for each of a plurality of categories, determine one or more concepts associated with a conceptual description of the category and generate a weak annotator for each of the one or more concepts. The electronic processor is also configured to apply each weak annotator to each training data example and, when a training data example satisfies a weak annotator, output a category associated with the weak annotator. For each training data example, the electronic processor determines a probabilistic distribution of the plurality of categories. For each training data example, the electronic processor labels the training data example with a category having the highest value in the probabilistic distribution determined for the training data example.