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Recognizing Textual Entailment based on Deep Learning Approach

by Mohamed H. Haggag, Marwa M. A. ELFattah, Ahmed Mohammed Ahmed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 43
Year of Publication: 2019
Authors: Mohamed H. Haggag, Marwa M. A. ELFattah, Ahmed Mohammed Ahmed
10.5120/ijca2019918515

Mohamed H. Haggag, Marwa M. A. ELFattah, Ahmed Mohammed Ahmed . Recognizing Textual Entailment based on Deep Learning Approach. International Journal of Computer Applications. 181, 43 ( Mar 2019), 36-41. DOI=10.5120/ijca2019918515

@article{ 10.5120/ijca2019918515,
author = { Mohamed H. Haggag, Marwa M. A. ELFattah, Ahmed Mohammed Ahmed },
title = { Recognizing Textual Entailment based on Deep Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 181 },
number = { 43 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number43/30406-2019918515/ },
doi = { 10.5120/ijca2019918515 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:58.932383+05:30
%A Mohamed H. Haggag
%A Marwa M. A. ELFattah
%A Ahmed Mohammed Ahmed
%T Recognizing Textual Entailment based on Deep Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 43
%P 36-41
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. This paper proposes new model based on deep learning approach to recognize textual entailment. The deep learning approach is based on syntactic structure [Holder- Relation - Target] [1] which contains all lexical, syntactic and semantic information about the input text. The proposed model constructs deep leaning neural networks, which aims at building deep and complex encoder to transform a sentence into encoded vectors. The experimental results demonstrate that proposed technique is effective to solve the problem of textual entailment recognition

References
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