2025
EMNLP
EMNLP 2025
An Address Intelligence Framework for E-commerce Deliveries
Abstract
AbstractFor an e-commerce domain, the customeraddress is the single most important pieceof customer data for ensuring accurateand reliable deliveries. In this two-partstudy, we first outline the construction ofa language model to assist customers withaddress standardization and in the latterpart, we detail a novel Pareto-ensemblemulti-task prediction algorithm that derives critical insights from customer addresses to minimize operational losses arising from a given geographical area. Finally, we demonstrate the potential benefits ofthe proposed address intelligence systemfor a large e-commerce domain throughlarge scale experiments on a commercialsystem.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— multi-task prediction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Applications > Information Extraction
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Core Methods > Multi-Task Learning
Machine Learning > Core Methods > Ensemble Methods
Machine Learning > Learning Types > Retrieval-Augmented Generation
Machine Learning > Learning Paradigms > Multi-Task Learning
Artificial Intelligence > Core AI > Language
Deep Learning > Learning Types > Multi-Task Learning
Artificial Intelligence > Core AI > Natural Language Processing