Automated Address Matching via NLP Entity Extraction
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Solution Overview
Problem
In the shipping cycle, erroneous addresses in Shipping Instructions often cannot be successfully mapped due to variations, errors, or misspellings, leading to inconsistent and time-consuming manual handling.
Innovation Solution
An automated address matching method using entity extraction and natural language processing techniques to determine similarity scores and weights, enabling efficient and accurate matching of addresses between different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual handling of erroneous addresses is performed, then flexibility in addressing variations is maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The system performs self-service by automatically detecting, analyzing, and correcting address errors using NLP and entity extraction techniques, eliminating the need for manual intervention while maintaining high accuracy in handling address variations
Solution Approach 2:
The manual mechanical process of address verification is replaced with an automated electronic system using natural language processing, entity extraction, and machine learning algorithms to detect and correct address errors automatically
2Adaptability or versatility
If manual handling of erroneous addresses is performed, then complex address variations can be addressed, but cost and time efficiency deteriorate
Solution Approach 1:
The system changes parameters by using weighted scoring mechanisms and adjustable thresholds in the NLP model to adapt to different address variation patterns, maintaining versatility while achieving automated high-speed processing
Solution Approach 2:
The manual adaptive process is replaced with an electronic system that uses machine learning and NLP to automatically adapt to various address formats, spellings, and structures at high processing speeds
3Productivity
If automated address matching is implemented, then processing speed and consistency improve, but complexity of the system increases
Solution Approach 1:
The automated address matching system is segmented into distinct functional modules: entity extraction module, NLP processing module, weighting module, and matching module. This segmentation manages system complexity by making each component independent and manageable while maintaining high overall processing speed and consistency
4Loss of time
If automated address matching is implemented, then time efficiency improves, but accuracy may be compromised due to address variations
Solution Approach 1:
The system incorporates feedback mechanisms where the NLP model continuously learns from matching results and adjusts its weighting parameters to improve accuracy over time, maintaining high precision while processing addresses rapidly
Solution Approach 2:
The system dynamically changes parameters such as similarity thresholds and element weights based on the specific address being processed, allowing it to maintain high accuracy across diverse address variations while operating at automated speeds
Data Source
AI summary
Disclosed is a method, performed by an electronic device, for address matching. The method comprises obtaining a first data set indicative of a first address. The method comprises determining, based on the first data set, using an entity extraction technique, one or more first elements indicative of the first address. The method comprises obtaining based on at least one of the one or more first elements, a second data set. The method comprises the second data set comprises one or more second elements indicative of a second address. The method comprises applying a natural language processing, NLP, technique to the first data set and the second data set, to obtain one or more similarity scores. The method comprises obtaining a set of weights associated with corresponding first elements of the first address.