research-article

Disambiguating Arabic Words According to Their Historical Appearance in the Document Based on Recurrent Neural Networks

Authors:

Chafik Aloulou,

Lamia Hadrich BelguithAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 19, Issue 6

Article No.: 86, Pages 1 - 16

https://doi.org/10.1145/3410569

Published: 15 October 2020 Publication History

Abstract

How can we determine the semantic meaning of a word in relation to its context of appearance? We eventually have to grabble with this difficult question, as one of the paramount problems of Natural Language Processing (NLP). In other words, this issue is commonly defined as Word Sense Disambiguation (WSD). The latter is one of the crucial difficulties within the NLP field. In this respect, word vectors extracted from a neural network model have been successfully applied for resolving the WSD problem. Accordingly, this article presents an unprecedented method to disambiguate Arabic words according to both their contextual appearance in a source text and the era in which they emerged. In fact, in the few previous decades, many researchers have been grabbling with Arabic Word Sense Disambiguation.

It should be noted that the Arabic language can be divided into three major historical periods: old Arabic, middle-age Arabic, and contemporary Arabic. Actually, contemporary Arabic has proved to be the greatest concern of many researchers. The main gist of our work is to disambiguate Arabic words according to the historical period in which they appeared. To perform such a task, we suggest a method that deploys contextualized word embeddings to better gather valid syntactic and semantic information of the same word by taking into account its contextual uses. The preponderant thing is to convert both the senses and the contextual uses of an ambiguous item to vectors, then determine which of the possible conceptual meanings of the target word is closer to the given context.

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Djaidri AAliane HAzzoune H(2023)The Contribution of Selected Linguistic Markers for Unsupervised Arabic Verb Sense DisambiguationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360577722:8(1-23)Online publication date: 24-Aug-2023
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Belhadi ADjenouri YSrivastava GLin J(2023)Fast and Accurate Framework for Ontology Matching in Web of ThingsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357870822:5(1-19)Online publication date: 17-Jan-2023
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Laatar RRhayem AAloulou CBelguith L(2022)Towards a Historical Ontology for Arabic Language: Investigation and Future DirectionsIntelligent Systems Design and Applications10.1007/978-3-030-96308-8_100(1078-1087)Online publication date: 27-Mar-2022
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Index Terms

Disambiguating Arabic Words According to Their Historical Appearance in the Document Based on Recurrent Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics

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Information & Contributors

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 19, Issue 6

November 2020

277 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3426881

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

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Publication History

Published: 15 October 2020

Accepted: 01 July 2020

Revised: 01 May 2020

Received: 01 February 2020

Published in TALLIP Volume 19, Issue 6

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Cited By

Djaidri AAliane HAzzoune H(2023)The Contribution of Selected Linguistic Markers for Unsupervised Arabic Verb Sense DisambiguationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360577722:8(1-23)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3605777
Belhadi ADjenouri YSrivastava GLin J(2023)Fast and Accurate Framework for Ontology Matching in Web of ThingsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357870822:5(1-19)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3578708
Laatar RRhayem AAloulou CBelguith L(2022)Towards a Historical Ontology for Arabic Language: Investigation and Future DirectionsIntelligent Systems Design and Applications10.1007/978-3-030-96308-8_100(1078-1087)Online publication date: 27-Mar-2022
https://doi.org/10.1007/978-3-030-96308-8_100
El-Razzaz MFakhr MMaghraby F(2021)Arabic Gloss WSD Using BERTApplied Sciences10.3390/app1106256711:6(2567)Online publication date: 13-Mar-2021
https://doi.org/10.3390/app11062567
Hou K(2021)Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural NetworkWireless Communications & Mobile Computing10.1155/2021/24386562021Online publication date: 1-Jan-2021
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Neji SChenaina TShoeb ABen Ayed L(2021)HIR: A Hybrid IR Ranking Model2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00256(1717-1722)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00256
Neji SChenaina TShoeb AAyed L(2021)SemApp: A Semantic Approach to Enhance Information RetrievalComputational Science and Its Applications – ICCSA 202110.1007/978-3-030-86970-0_6(62-78)Online publication date: 11-Sep-2021
https://doi.org/10.1007/978-3-030-86970-0_6

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