Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End
  Information Extraction   We address the challenge of extracting structured information from business
documents without detailed annotations. We propose Deep Conditional
Probabilistic Context Free Grammars (DeepCPCFG) to parse two-dimensional
complex documents and use Recursive Neural Networks to create an end-to-end
system for finding the most probable parse that represents the structured
information to be extracted. This system is trained end-to-end with scanned
documents as input and only relational-records as labels. The
relational-records are extracted from existing databases avoiding the cost of
annotating documents by hand. We apply this approach to extract information
from scanned invoices achieving state-of-the-art results despite using no
hand-annotations.

20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph