BERT-Based Validation for Semantically Weak Classification Codes
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Solution Overview
Problem
Conventional AI models struggle to accurately assign classification codes to data objects due to the lack of semantic structure in classification codes, leading to reduced accuracy and quality in predictive models.
Innovation Solution
A second AI model is used to validate classification codes assigned by a first AI model, utilizing a deep-learning based semi-supervised framework that includes a BERT model, FFN, and contrastive loss function to learn semantic representations and differentiate between correct and incorrect classification codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a first AI model assigns classification codes to data objects, then classification coding is automated, but accuracy and quality are reduced due to lack of semantic structure in classification codes
Solution Approach 1:
The system introduces a validation mechanism where a second AI model provides feedback on the classification codes assigned by the first AI model. The validation model receives the data object and the assigned classification code, then outputs a validation result indicating whether the code is correct or incorrect. This feedback loop enables continuous improvement of classification accuracy without sacrificing automation.
Solution Approach 2:
The validation AI model acts as an intermediary between the data object and the classification code assignment. Instead of directly assigning codes, the system uses the validation model to verify and validate the codes, creating a mediating layer that improves accuracy while maintaining automated operation.
2Ease of manufacture
If classification codes are used without semantic structure, then coding simplicity is maintained, but quality of AI model predictions is reduced
Solution Approach 1:
The validation model provides feedback that helps improve the reliability of predictive models by identifying and correcting inaccurate classification codes. This feedback mechanism allows the system to maintain simple coding structures while improving prediction quality through continuous validation and correction.
Data Source
AI summary
Systems and methods for validating a classification code assigned to a data object by a first artificial intelligence (AI) model using a second AI model are provided. A data object associated with an entity including a classification code that is assigned to the data object via the first AI model can be received. The classification code for the data object that is assigned to the data object via the first AI model can be validated using the second AI model. The second AI model can be trained using positive data objects that include assigned classification codes that are correct and negative data objects that include assigned classification codes that are incorrect. A validation result can be transmitted to a user device based on validating the classification code for the data object using the second AI model.