Training Data Update for Natural Language Classifiers
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Solution Overview
Problem
The quality of customer support provided by Natural Language Classifiers (NLCs) is affected by the accuracy and precision of training data, which is labor-intensive and prone to fluctuations due to variations in expert classification, leading to overtraining or high computational demands.
Innovation Solution
A method is introduced to update training data by dividing questions into groups, classifying them using multiple classifiers, and iteratively refining the classification results until a predetermined threshold of matching is achieved, thereby improving the precision and reducing overtraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is prepared manually by subject matter experts to ensure high accuracy and precision, then the quality of customer support provided by NLCs is improved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent divides the training data preparation process into multiple iterations, where each iteration processes a subset of questions and refines classifications incrementally. This segmentation allows the system to achieve high accuracy through progressive refinement rather than requiring complete manual preparation of all training data at once, thereby reducing the overall time investment while maintaining precision.
Solution Approach 2:
The patent implements a feedback mechanism where classification results are evaluated against expected outcomes, and discrepancies are used to refine training data in subsequent iterations. This automated feedback loop enables the system to improve classification accuracy progressively without requiring continuous manual intervention, thus resolving the contradiction between achieving high precision and reducing time investment.
2Reliability
If training data is refined manually to reduce fluctuations due to expert variations, then classification reliability is improved, but the process becomes more labor-intensive
Solution Approach 1:
The patent enables the training data preparation system to serve itself by automatically identifying discrepancies between expected and actual classification results, and autonomously refining training data based on these discrepancies. This self-service capability eliminates the need for continuous manual refinement while ensuring consistent classification reliability across different runs, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The patent systematically adjusts training data parameters such as question formulations, answer mappings, and classification thresholds based on performance metrics from previous iterations. These parameter changes are made automatically to reduce fluctuations in classification results, improving reliability while the systematic approach keeps the overall process complexity manageable through algorithmic rather than manual adjustments.
3Measurement precision
If extensive training data is used to improve NLC accuracy, then classification precision is improved, but computational resources and training time increase
Solution Approach 1:
The patent applies partial action by processing and refining only the necessary subset of training data in each iteration, rather than repeatedly processing the entire training corpus. By focusing computational efforts on specific questions and classifications that need refinement, the system achieves improved precision without proportionally increasing overall computational resource consumption.
Solution Approach 2:
The patent implements periodic action through iterative refinement cycles, where training data is processed, evaluated, and updated in discrete stages rather than through continuous exhaustive processing. This periodic approach allows the system to achieve high classification precision through multiple targeted passes, reducing total computational resource consumption compared to single-pass exhaustive methods.
4Measurement precision
If manual training data updates are performed frequently to maintain accuracy, then classification accuracy is maintained, but productivity decreases
Solution Approach 1:
The patent implements automated feedback mechanisms that continuously monitor classification performance and trigger updates only when necessary. This feedback-driven approach maintains high training data accuracy by performing updates based on actual performance needs rather than on a fixed schedule, thereby maintaining precision while improving productivity by avoiding unnecessary update cycles.
Solution Approach 2:
The patent enables the system to autonomously detect when training data updates are needed based on performance metrics, and automatically performs the updates without requiring manual intervention. This self-service capability maintains high classification accuracy while significantly improving productivity by eliminating the need for frequent manual review and updating of training data.
Data Source
AI summary
Training data including a first and second group of questions each associated with an answer is read into memory by a computer. A class of answers including the answer to a question for each question is determined, where each class of answers has a class label that is associated with each of the questions, and each of the questions are classified into a respective class of answers, accordingly. First and second training data is generated including the first and second groups of questions and corresponding classes of answers for use in first and second classifiers, respectively. Each question of the first and second group of questions is classified by the second and first classifiers, respectively, where the classifying generates corresponding classification results. The first or second training data is updated based on the classification results to generate corresponding updated first or second training data, respectively.