Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards the Improvement of Automated Scientific Document Categorization
  by Deep Learning   This master thesis describes an algorithm for automated categorization of
scientific documents using deep learning techniques and compares the results to
the results of existing classification algorithms. As an additional goal a
reusable API is to be developed allowing the automation of classification tasks
in existing software. A design will be proposed using a convolutional neural
network as a classifier and integrating this into a REST based API. This is
then used as the basis for an actual proof of concept implementation presented
as well in this thesis. It will be shown that the deep learning classifier
provides very good result in the context of multi-class document categorization
and that it is feasible to integrate such classifiers into a larger ecosystem
using REST based services.

10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph