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Arabic Sentences Classification via Deep Learning
by
Dania Sagheer,
Fadel Sukkar
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 5 |
| Year of Publication: 2018 |
| Authors: Dania Sagheer, Fadel Sukkar |
10.5120/ijca2018917555
|
Dania Sagheer, Fadel Sukkar . Arabic Sentences Classification via Deep Learning. International Journal of Computer Applications. 182, 5 ( Jul 2018), 40-46. DOI=10.5120/ijca2018917555
@article{
10.5120/ijca2018917555,
author = {
Dania Sagheer,
Fadel Sukkar
},
title = { Arabic Sentences Classification via Deep Learning },
journal = {
International Journal of Computer Applications
},
issue_date = { Jul 2018 },
volume = { 182 },
number = { 5 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = {
40-46
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume182/number5/29762-2018917555/
},
doi = { 10.5120/ijca2018917555 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:31.210157+05:30
%A Dania Sagheer
%A Fadel Sukkar
%T Arabic Sentences Classification via Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 5
%P 40-46
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a Convolutional Neural Network CNN Models to classify Arabic sentences into three topics. These sentences are derived from Essex Arabic Summaries Corpus (EASC) corpus, tokenized to words and transformed to sequences of word indices. All sequences are padded to be in the same length. The models of Convolution Neural Network are built on top of word embedding layer. The word embedding layer is either pre-trained or jointed into the model. Dropout and l2 weight regularization are used to overcome the overfitting during training. The CNN models achieve high performance in accuracy for Arabic sentences classification.
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Index Terms
Computer Science
Information Sciences
Keywords