Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon
  construction   Generating semantic lexicons semi-automatically could be a great time saver,
relative to creating them by hand. In this paper, we present an algorithm for
extracting potential entries for a category from an on-line corpus, based upon
a small set of exemplars. Our algorithm finds more correct terms and fewer
incorrect ones than previous work in this area. Additionally, the entries that
are generated potentially provide broader coverage of the category than would
occur to an individual coding them by hand. Our algorithm finds many terms not
included within Wordnet (many more than previous algorithms), and could be
viewed as an ``enhancer'' of existing broad-coverage resources.

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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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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: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: hep-ex