Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representations of Language Varieties Are Reliable Given Corpus
  Similarity Measures   This paper measures similarity both within and between 84 language varieties
across nine languages. These corpora are drawn from digital sources (the web
and tweets), allowing us to evaluate whether such geo-referenced corpora are
reliable for modelling linguistic variation. The basic idea is that, if each
source adequately represents a single underlying language variety, then the
similarity between these sources should be stable across all languages and
countries. The paper shows that there is a consistent agreement between these
sources using frequency-based corpus similarity measures. This provides further
evidence that digital geo-referenced corpora consistently represent local
language varieties.

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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio