Training Data Generation Apparatus Clustering for Annotation Cost Reduction
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Solution Overview
Problem
The cost of creating ground truth data for characteristic expression recognition remains high due to the need for accurate and omission-free annotation, which is a costly and time-consuming process.
Innovation Solution
A training data generation apparatus that clusters training data candidates based on context information and identifies suitable candidates using distribution analysis, allowing for the generation of training data without the need for accurate and omission-free annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed to create ground truth data, then annotation accuracy is improved, but creation cost increases
Solution Approach 1:
The system performs self-annotation by automatically generating training data candidates from unannotated texts and using clustering algorithms to assign labels, eliminating the need for manual human annotation while maintaining data quality through automated consistency checks
Solution Approach 2:
The system creates training data candidates by copying and transforming existing ground truth data through operations such as word order changes, syntactic representation conversion, and specific representation conversion, thereby generating expanded training data without requiring new manual annotation
2Reliability
If manual verification is performed to ensure omission-free annotation, then data quality is improved, but processing time increases
Solution Approach 1:
The system implements automated feedback mechanisms where clustering results are used to identify and correct potential annotation errors and omissions, continuously improving data quality through iterative processing without requiring manual verification at each step
Solution Approach 2:
The system performs preliminary clustering and validation operations on training data candidates before final use, automatically detecting and correcting annotation issues in advance, thereby ensuring data quality without time-consuming manual verification during deployment
3Ease of manufacture
If training data is generated without accurate annotation, then creation cost is reduced, but training data quality deteriorates
Solution Approach 1:
The system replaces the mechanical process of manual annotation with automated computational methods including clustering algorithms and distribution analysis, which process training data candidates at scale while maintaining consistent quality standards through algorithmic enforcement rather than human judgment
Solution Approach 2:
The system changes the parameters of training data generation by using automated clustering based on feature values and context information, adjusting the label assignment process from manual human decisions to algorithmic determination based on data distribution patterns, thereby maintaining quality while reducing cost
Data Source
AI summary
The disclosed apparatus uses a training data generation apparatus 2, which generates training data used for creating characteristic expression extraction rules. The training data generation apparatus 2 includes: a training data candidate clustering unit 21, which clusters a plurality of training data candidates assigned labels indicating annotation classes based on feature values containing respective context information, and a training data generation unit 22 which, by referring to each cluster obtained using the clustering results, obtains the distribution of the labels of the training data candidates within the cluster, identifies training data candidates that meet a preset condition based on the obtained distribution, and generates training data using the identified training data candidates.