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A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning
by
Mohamed I. El Desouki,
Wael H. Gomaa,
Hawaf Abdalhakim
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 20 |
| Year of Publication: 2019 |
| Authors: Mohamed I. El Desouki, Wael H. Gomaa, Hawaf Abdalhakim |
10.5120/ijca2019919011
|
Mohamed I. El Desouki, Wael H. Gomaa, Hawaf Abdalhakim . A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning. International Journal of Computer Applications. 178, 20 ( Jun 2019), 18-23. DOI=10.5120/ijca2019919011
@article{
10.5120/ijca2019919011,
author = {
Mohamed I. El Desouki,
Wael H. Gomaa,
Hawaf Abdalhakim
},
title = { A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning },
journal = {
International Journal of Computer Applications
},
issue_date = { Jun 2019 },
volume = { 178 },
number = { 20 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = {
18-23
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume178/number20/30650-2019919011/
},
doi = { 10.5120/ijca2019919011 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:50:56.929303+05:30
%A Mohamed I. El Desouki
%A Wael H. Gomaa
%A Hawaf Abdalhakim
%T A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 20
%P 18-23
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Paraphrase detection (PD) is a very essential and important task in Natural language processing. The goal of paraphrase detection is to check whether two statements written in natural language have the identical semantic or not. Its importance appears in many fields like plagiarism detection, question answering, document clustering and information retrieval, etc. This paper proposes a hybrid model that combines the text similarity approach with deep learning approach in order to improve paraphrase detection. This model verified results with Microsoft Research Paraphrase Corpus (MSPR) dataset, shows that accuracy measure is about 76.6% and F-measure is about 83.5%.
References
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