Address Substring Classification for Standardized Formatting
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Solution Overview
Problem
Existing address verification and translation systems fail to process poorly formatted or inconsistent address strings, leading to incorrect location identification of places-of-interest, as they require a minimum number of address components and cannot handle duplicates or non-compliant signatures, such as intersection addresses.
Innovation Solution
A system that classifies substrings as address or non-address substrings using a trained machine learning model, normalizes and partitions address substrings, and applies jurisdiction-specific formatting rules to produce a standardized address, capable of handling inconsistent and non-compliant address formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing address verification systems require a minimum number of address components, then they can ensure basic address completeness, but they fail to process poorly formatted or inconsistent address strings
Solution Approach 1:
The system segments the address string into multiple substrings and classifies each substring as either an address substring or a non-address substring. This segmentation allows the system to process poorly formatted addresses by identifying and extracting valid address components regardless of their position or formatting in the original string.
Solution Approach 2:
The system changes the parameter of address component requirements from fixed minimum requirements to flexible classification-based requirements. By using machine learning classification rather than rigid component counting, the system can adapt to various address formats while maintaining verification accuracy.
2Adaptability or versatility
If the system processes all substrings as potential address components, then it can handle diverse formats, but it cannot distinguish between address and non-address substrings leading to incorrect addresses
Solution Approach 1:
The system uses a trained machine learning model that has learned from training data to classify substrings. The model provides feedback-based classification by analyzing patterns in the substrings and determining whether they represent address components or non-address elements, thereby improving identification accuracy across diverse formats.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw address string and the final standardized address. It processes the substrings through classification, separating address substrings from non-address substrings, and enables accurate identification of address components without requiring manual rules for each format variation.
3Reliability
If the system uses traditional address verification rules, then it can ensure compliance with jurisdictional standards, but it cannot handle intersection addresses or duplicate substrings
Solution Approach 1:
The system dynamically adapts its processing based on the content of each substring. Rather than applying static rules, the machine learning model dynamically classifies substrings based on their characteristics, allowing the system to handle various address types including intersection addresses and addresses with duplicate substrings while maintaining compliance with jurisdictional standards.
Solution Approach 2:
The machine learning classification system provides a universal approach that can handle multiple types of address formats including traditional addresses, intersection addresses, and addresses with duplicates. The same classification framework applies to all address types, making the system multi-functional and adaptable to various jurisdictional requirements.
Data Source
AI summary
Standardized address generation from address substrings includes receiving an address string for a place-of-interest, one-to-many mapping at least one of a plurality of address substrings of the address string to respective address components, concatenating the address substrings using a template that specifies an order of concatenating the address substrings, and making the concatenated address substrings available for further use.