Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Syntax-Based Machine Translation to Parse English into Abstract
  Meaning Representation   We present a parser for Abstract Meaning Representation (AMR). We treat
English-to-AMR conversion within the framework of string-to-tree, syntax-based
machine translation (SBMT). To make this work, we transform the AMR structure
into a form suitable for the mechanics of SBMT and useful for modeling. We
introduce an AMR-specific language model and add data and features drawn from
semantic resources. Our resulting AMR parser improves upon state-of-the-art
results by 7 Smatch points.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
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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: stat
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The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-ex