Lot-insts (Long-Tailed instituition names)
Introduced by Qi et al. in Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
LoT-insts contains over 25k classes whose frequencies are naturally long-tail distributed. Its test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set. To our best knowledge, this is the first natural language dataset that focuses on this long-tailed and open classification problem.
Benchmarks
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Lot-insts
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Character-BERT+RS
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Lot-insts
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Character-BERT+RS
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Papers
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