Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Question Answering over Unstructured Data without Domain Restrictions   Information needs are naturally represented as questions. Automatic
Natural-Language Question Answering (NLQA) has only recently become a practical
task on a larger scale and without domain constraints.
  This paper gives a brief introduction to the field, its history and the
impact of systematic evaluation competitions.
  It is then demonstrated that an NLQA system for English can be built and
evaluated in a very short time using off-the-shelf parsers and thesauri. The
system is based on Robust Minimal Recursion Semantics (RMRS) and is portable
with respect to the parser used as a frontend. It applies atomic term
unification supported by question classification and WordNet lookup for
semantic similarity matching of parsed question representation and free text.

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The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg