Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings
  and Grammatical Errors   The lack of large-scale datasets has been a major hindrance to the
development of NLP tasks such as spelling correction and grammatical error
correction (GEC). As a complementary new resource for these tasks, we present
the GitHub Typo Corpus, a large-scale, multilingual dataset of misspellings and
grammatical errors along with their corrections harvested from GitHub, a large
and popular platform for hosting and sharing git repositories. The dataset,
which we have made publicly available, contains more than 350k edits and 65M
characters in more than 15 languages, making it the largest dataset of
misspellings to date. We also describe our process for filtering true typo
edits based on learned classifiers on a small annotated subset, and demonstrate
that typo edits can be identified with F1 ~ 0.9 using a very simple classifier
with only three features. The detailed analyses of the dataset show that
existing spelling correctors merely achieve an F-measure of approx. 0.5,
suggesting that the dataset serves as a new, rich source of spelling errors
that complement existing datasets.

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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin