Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Object-oriented Neural Programming (OONP) for Document Understanding   We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc