Skip to main navigation menu Skip to main content Skip to site footer
Español (España) | English
Editorial
Revistas / Journals
Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home
Issue
Current Issue Previous Issues Thematic issues
About
About the Journal Editorial Team Privacy Statement Contact
Author Guidelines
Guidelines for authors Peer Review Process Open Access Policy and Copyright Digital preservation policy Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
×
Español (España) | English
Editorial

Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home

Current Issue Previous Issues Thematic issues

About the Journal Editorial Team Privacy Statement Contact

Guidelines for authors Peer Review Process Open Access Policy and Copyright Digital preservation policy Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
Original
Published: 2023-10-10
DOI: https://doi.org/10.56294/sctconf2023472

Deep Learning Applied on Arabic language for punctuation marks prediction

By
Abdelkarim Aboutaib ,
Abdelkarim Aboutaib

L-STI, T-IDMS, FST Errachidia, Moulay Ismail University of Meknes, Morocco.

Search this author on:

PubMed | Google Scholar
Imad Zeroual ,
Imad Zeroual

L-STI, T-IDMS, FST Errachidia, Moulay Ismail University of Meknes, Morocco.

Search this author on:

PubMed | Google Scholar
Ahmad EL Allaoui ,
Ahmad EL Allaoui

L-STI, T-IDMS, FST Errachidia, Moulay Ismail University of Meknes, Morocco.

Search this author on:

PubMed | Google Scholar

Abstract

In the absence of explicit punctuation, the Arabic language's semantic and contextual nature poses a unique challenge, necessitating the reintroduction of punctuation marks for elucidating sentence structure and meaning. We investigate the impact of sentence length on punctuation prediction in the context of Arabic language processing. Leveraging Deep Neural Networks (DNNs), specifically Bi-Directional Long Short-Term Memory (Bi-LSTM) models. Our study goes beyond restoration, aiming to accurately predict punctuation marks in unprocessed text. The investigation focuses on five primary punctuation marks (.?,: and !), contributing to a more comprehensive understanding of predicting diverse punctuation marks in Arabic texts and we have achieved 85 % in accuracy . This research not only advances our understanding of Arabic language processing but also serves as a broader exploration of the relationship between sentence length and punctuation prediction.

 

Keywords:

Deep Learning,
Bi-LSTM,
NLP,
Attention,

How to Cite

1.
Aboutaib A, Zeroual I, EL Allaoui A. Deep Learning Applied on Arabic language for punctuation marks prediction. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2023 Oct. 10 [cited 2024 Jul. 3];2:472. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/472

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

IV Jornadas de Fundamentos y Aplicaciones de la Interdisciplina (JFAI2023)
Vol. 2 (2023)
See full issue

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.