Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Benchmarking Top-K Keyword and Top-K Document Processing with
  T${}^2$K${}^2$ and T${}^2$K${}^2$D${}^2$   Top-k keyword and top-k document extraction are very popular text analysis
techniques. Top-k keywords and documents are often computed on-the-fly, but
they exploit weighted vocabularies that are costly to build. To compare
competing weighting schemes and database implementations, benchmarking is
customary. To the best of our knowledge, no benchmark currently addresses these
problems. Hence, in this paper, we present T${}^2$K${}^2$, a top-k keywords and
documents benchmark, and its decision support-oriented evolution
T${}^2$K${}^2$D${}^2$. Both benchmarks feature a real tweet dataset and queries
with various complexities and selectivities. They help evaluate weighting
schemes and database implementations in terms of computing performance. To
illustrate our bench-marks' relevance and genericity, we successfully ran
performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand,
and on different relational (Oracle, PostgreSQL) and document-oriented
(MongoDB) database implementations, on the other hand.

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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th