Ensemble Email Validation Models for Gibberish Address Detection
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Solution Overview
Problem
Distinguishing between valid and invalid email addresses is challenging due to the presence of gibberish and non-gibberish email addresses, which complicates existing validation methods, and requesting return receipts is impractical or unreliable.
Innovation Solution
Training multiple language models, such as BERT models with fully connected and softmax layers, to classify email addresses as gibberish or non-gibberish, and then fine-tuning these models to predict the validity of each type, using an ensemble approach for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If email validation is performed by sending confirmation emails and requesting read receipts, then email validity can be verified, but the process becomes impractical, unreliable, and may be spoofed
Solution Approach 1:
The validation system segments email addresses into two distinct categories: gibberish email addresses (random characters, invalid formats) and non-gibberish email addresses (legitimate formats). Different validation approaches are applied to each segment, with gibberish addresses being immediately flagged as invalid without sending confirmation emails, thereby improving reliability while reducing operational complexity
Solution Approach 2:
The patent introduces an intermediary validation mechanism that analyzes email address characteristics (format, domain existence, MX records) before sending confirmation emails. This intermediary layer filters out obviously invalid addresses, making the overall validation process more reliable and less prone to spoofing while reducing the number of confirmation emails that need to be sent
2Productivity
If gibberish email addresses are automatically labeled as invalid, then processing efficiency improves, but valid gibberish email addresses may be misclassified
Solution Approach 1:
The system segments the validation process into two stages: first, gibberish detection that quickly identifies obviously invalid addresses; second, a more thorough validation process for addresses that pass the initial filter. This segmentation maintains high productivity by quickly dismissing clear gibberish while preserving measurement precision through the secondary validation stage
Solution Approach 2:
The patent adjusts validation parameters dynamically based on the detected email type. For gibberish addresses, stricter format requirements are applied with lower thresholds for rejection. For non-gibberish addresses, more lenient parameters are used, allowing valid addresses with slight formatting variations to pass through, thereby maintaining both productivity and precision
3Measurement precision
If multiple language models are trained separately on gibberish and non-gibberish email addresses, then classification accuracy improves, but model complexity increases
Solution Approach 1:
The model ensemble is segmented into specialized sub-models: one trained specifically on gibberish email patterns and another on non-gibberish patterns. Each model focuses on its designated segment, achieving higher classification accuracy for its specific type while the overall system manages complexity through modular architecture that allows independent training and deployment of each component
Data Source
AI summary
A method for training an ensemble machine learning model. The method includes applying a first language model to a training data set, having emails stored in a non-transitory computer readable storage medium, to split email addresses in the emails into gibberish email addresses and non-gibberish email addresses. The gibberish email addresses include a first text string that the first language model classifies as gibberish. The non-gibberish email addresses include a second text string that the first language model classifies as non-gibberish. The method also includes training a second language model on the gibberish email addresses. The second language model is trained to determine whether the gibberish email addresses are valid or invalid. The method also includes training a third language model on the non-gibberish email addresses. The third language model is trained to determine whether the non-gibberish email addresses are valid or invalid.