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LSH methods for data deduplication in a Wikipedia artificial dataset

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arxiv 2112.11478 v1 pith:IPHUXZUD submitted 2021-12-10 cs.CL cs.IRcs.LG

Juan Ciro , Daniel Galvez , Tim Schlippe , David Kanter This is my paper · ORCID
classification cs.CLcs.IRcs.LG
keywords datadatasetdeduplicationmodelmodelsartificialwikipediaarea-under-curve
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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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