Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Attentional Hybrid Neural Networks for Document
  Classification   Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom