Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Architecture for Semantic Parsing   Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.

18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
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The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: nlin
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga