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We describe NatCat, a large-scale resource for text classificationconstructed from three data sources: Wikipedia, Stack Exchange, and Reddit.NatCat consists of document-category pairs derived from manual curation thatoccurs naturally within online communities. To demonstrate its usefulness, webuild general purpose text classifiers by training on NatCat and evaluate themon a suite of 11 text classification tasks (CatEval), reporting largeimprovements compared to prior work. We benchmark different modeling choicesand resource combinations and show how tasks benefit from particular NatCatdata sources.

NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources