Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Language from a Large (Unannotated) Corpus   A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.

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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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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: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an