natural-language-processing
Blocking
3260 papers • 126 benchmarks • 313 datasets
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. Survey on blocking: Papadakis et al.: Blocking and Filtering Techniques for Entity Resolution: A Survey, 2020.
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Most implemented papers
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Y. Yoo, Nojun Kwak, Hyojin Park, Lars Lowe Sjösund, Nicolas Monet•Tue Nov 19 2019
The new extremely lightweight portrait segmentation model SINet is introduced, containing an information blocking decoder and spatial squeeze modules, and it is demonstrated that the method can be used for general semantic segmentation on the Cityscapes dataset.
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