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arXiv · 2112.11478

LSH methods for data deduplication in a Wikipedia artificial dataset

Juan Ciro·Daniel Galvez·Tim Schlippe·David Kanter
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Abstract

This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset. To evaluate the different models, we create an artificial dataset for data deduplication using English Wikipedia articles. Area-Under-Curve (AUC) over 0.9 were observed for most models, with the best model reaching 0.96. Deduplication enables more effective model training by preventing the model from learning a distribution that differs from the real one as a result of the repeated data.

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Juan Ciro, Daniel Galvez, Tim Schlippe, David Kanter. 2021-12-10. LSH methods for data deduplication in a Wikipedia artificial dataset. https://arxiv.org/abs/2112.11478

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