Ground Truth Generation for ML Quality Assessment
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Solution Overview
Problem
Current manual ground truth generation for quality assessment of articles is time-intensive and not scalable, prone to human error and bias, and fails to control for article topics, leading to inaccurate quality classification.
Innovation Solution
A method and system for ground truth generation using topic clustering and semi-supervised learning, where recommended and non-recommended articles are clustered by topic, and a quality assessment machine learning model is trained using a balanced training set to predict article quality independently of topic relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual ground truth generation is used for quality assessment, then expert knowledge can be applied, but the process is time-intensive and not scalable
Solution Approach 1:
The patent creates synthetic training data by copying and adapting existing high-quality articles. The system generates artificial low-quality versions of high-quality articles through controlled modifications, creating a scalable dataset without requiring manual expert assessment of every training example. This allows automated generation of large training sets while maintaining quality relationships.
Solution Approach 2:
The system uses the high-quality articles themselves to generate the training data. By automatically creating synthetic low-quality versions through text modification algorithms, the high-quality corpus serves its own purpose of generating training examples, eliminating the need for separate manual annotation processes.
2Ease of operation
If manual quality assessment is performed without topic control, then the process is simpler, but accuracy is reduced due to human error and bias
Solution Approach 1:
The patent segments the quality assessment task into two independent components: topic classification and quality assessment. By first clustering articles by topic and then assessing quality within each topic cluster, the system eliminates topic-related bias and allows for more precise quality measurements without increasing operational complexity.
Solution Approach 2:
Topic clustering serves as an intermediary step between raw article collection and quality assessment. This intermediate organization by topic controls for topic-related factors, allowing the quality assessment to focus purely on quality attributes without being confounded by topic differences, thereby improving measurement precision.
3Quantity of substance
If large training sets are generated manually, then data quality can be ensured, but the time and resources required increase significantly
Solution Approach 1:
The system generates large quantities of training data by copying high-quality articles and creating synthetic low-quality versions through automated text modification. This copying approach allows rapid generation of large training sets with consistent quality relationships, avoiding the time-consuming manual annotation process while maintaining data quality through controlled synthesis.
Solution Approach 2:
The patent performs preliminary topic clustering and high-quality article selection before generating the training set. By pre-organizing the corpus and identifying high-quality examples in advance, the system streamlines the subsequent synthetic data generation process, reducing overall time investment while enabling large-scale training set creation.
Data Source
AI summary
A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a ground truth generation system for quality assessment scoring of articles in a corpus. The ground truth generation system receives recommendations of a set of recommended articles from subject matter experts. The ground truth generation system identifies a set of non-recommended articles. A topic clustering component within the ground truth generation system performs topic clustering on a combination of the set of recommended articles and the set of non-recommended articles to form a set of topic clusters containing recommended articles and non-recommended articles. The ground truth generation system identifies a first number of recommended articles and a second number of non-recommended articles in each of the set of topic clusters to form a quality assessment training set. The mechanism trains a quality assessment machine learning model using the quality assessment training set.