Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What if we had no Wikipedia? Domain-independent Term Extraction from a
  Large News Corpus   One of the most impressive human endeavors of the past two decades is the
collection and categorization of human knowledge in the free and accessible
format that is Wikipedia. In this work we ask what makes a term worthy of
entering this edifice of knowledge, and having a page of its own in Wikipedia?
To what extent is this a natural product of on-going human discourse and
discussion rather than an idiosyncratic choice of Wikipedia editors?
Specifically, we aim to identify such "wiki-worthy" terms in a massive news
corpus, and see if this can be done with no, or minimal, dependency on actual
Wikipedia entries. We suggest a five-step pipeline for doing so, providing
baseline results for all five, and the relevant datasets for benchmarking them.
Our work sheds new light on the domain-specific Automatic Term Extraction
problem, with the problem at hand being a domain-independent variant of it.

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The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol