Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Light-Weighted CNN for Text Classification   For management, documents are categorized into a specific category, and to do
these, most of the organizations use manual labor. In today's automation era,
manual efforts on such a task are not justified, and to avoid this, we have so
many software out there in the market. However, efficiency and minimal resource
consumption is the focal point which is also creating a competition. The
categorization of such documents into specified classes by machine provides
excellent help. One of categorization technique is text classification using a
Convolutional neural network(TextCNN). TextCNN uses multiple sizes of filters,
as in the case of the inception layer introduced in Googlenet. The network
provides good accuracy but causes high memory consumption due to a large number
of trainable parameters. As a solution to this problem, we introduced a whole
new architecture based on separable convolution. The idea of separable
convolution already exists in the field of image classification but not yet
introduces to text classification tasks. With the help of this architecture, we
can achieve a drastic reduction in trainable parameters.

12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio