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Blocking

Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)

Blocking is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible. Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher generates a final set of matching record pairs.

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Papers

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Showing 341–350 of 524 papers

TitleStatusHype
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems—0
Learning to Use Learners' Advice—0
Leveraging Language Models for Automated Patient Record Linkage—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin—0
LithOS: An Operating System for Efficient Machine Learning on GPUs—0
Local SGD Meets Asynchrony—0
Long-distance anaphors and the blocking effect revisited-An East Asian perspective—0
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention—0
LOS/NLOS Estimators for mmWave Cellular Systems With Blockages—0
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