Information Extraction Support Device Using Weak Supervision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional information extraction systems require a large amount of manually labeled training examples for machine learning, which is labor-intensive and inefficient.
Innovation Solution
An information extraction support device that generates training examples using a weak supervision method, reducing the need for extensive manual labeling by deriving teaching rules from initial examples and applying them to other data, thereby reducing operator workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of manually labeled training examples are prepared for machine learning, then learning accuracy is improved, but operator workload and time consumption increase significantly
Solution Approach 1:
The system performs preliminary action by automatically generating candidate training examples and their labels before the actual machine learning process. The label generation unit creates initial labels based on document structure analysis and semantic understanding, providing a head start that reduces the need for extensive manual labeling later.
Solution Approach 2:
The system introduces an intermediary approach by using semi-automated label generation that combines machine-generated candidate labels with selective manual verification. This intermediary method bridges the gap between fully automated (potentially inaccurate) and fully manual (time-consuming) labeling approaches.
2Manufacturing precision
If extensive manual labeling of training examples is performed, then the quality of supervised data is improved, but productivity decreases due to labor-intensive work
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically generate its own training data and labels. The label generation unit uses the model's understanding of document structure and semantics to create labeled examples without requiring extensive human intervention, making the system self-sufficient in data preparation.
Solution Approach 2:
The system applies parameter changes by adjusting the balance between automated label generation and manual verification. The label generation unit can modify labeling parameters such as confidence thresholds and verification requirements to optimize the trade-off between data quality and generation efficiency based on specific task requirements.
3Measurement precision
If manual labeling of training examples is performed thoroughly, then extraction accuracy is improved, but the complexity of the preparation process increases
Solution Approach 1:
The system applies segmentation by dividing the labeling process into distinct modules: document structure analysis, candidate extraction, label generation, and verification. Each module handles a specific aspect of the labeling task, reducing overall process complexity while maintaining comprehensive coverage of labeling requirements.
Data Source
AI summary
An information extraction support device includes a receptor, a pattern generator, a data generator, and an output controller. The receptor receives input of a first training example for learning a model used in at least one of extraction of information and extraction of a relation between a plurality of pieces of information, and clue information indicating a basis on which the first training example is used for learning. The pattern generator generates a supervised pattern for generating a training example used for learning, using the first training example and the clue information. The data generator generates a second training example using the supervised pattern. The output controller outputs the second training example and the clue information that is used to generate the supervised pattern having generated the second training example.