Automatic Identification of Moroccan Colloquial Arabic

Tachicart, Ridouane; Bouzoubaa, Karim; Aouragh, Si Lhoussain; Jaafa, Hamid

doi:10.1007/978-3-319-73500-9_15

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…The authors use the MGB-3 (Multi-Genre Broadcast) dataset (Ali et al, 2017). Tachicart et al (2017) focused on an Identification system distinguishing between the Moroccan Dialect and MSA. The authors relied on two different approaches: (1) rule-based and (2) statistical-based (using several machine learning classifiers).…”

Section: Building Resourcesmentioning

confidence: 99%

Arabic natural language processing: An overview

Guellil

Saadane²,

Azouaou

et al. 2021

Journal of King Saud University - Computer and Information Scie

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a b s t r a c tArabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script (Arabizi), which corresponds to Arabic written with Latin letters, numerals and punctuation. Due to the complexity of this language and the number of corresponding challenges for NLP, many surveys have been conducted, in order to synthesise the work done on Arabic. However these surveys principally focus on two varieties of Arabic (MSA and AD, written in Arabic letters only), they are slightly old (no such survey since 2015) and therefore do not cover recent resources and tools. To bridge the gap, we propose a survey focusing on 90 recent research papers (74% of which were published after 2015). Our study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.

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Section: Building Resourcesmentioning

confidence: 99%

Arabic natural language processing: An overview

Guellil

Saadane²,

Azouaou

et al. 2021

Journal of King Saud University - Computer and Information Scie

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“…Table 3 shows all integrated resources for the Moroccan dialect. Concerning tools, a language identification system (Tachicart et al 2018) has been developed and integrated within SAFAR in order to distinguish between MD and MSA. Besides, we developed and integrated a spelling normalization systems that helps to convert a given Moroccan dialectal word into its standard form without taking into consideration the word context.…”

Section: Moroccan Dialectmentioning

confidence: 99%

“…After selecting the morphological analyzer to use via the drop-down menu (Alkhalil in this case) and clicking on the "Analyze & display" button, the output is displayed in a table format. Furthermore, the language identification system (Tachicart et al 2018) demonstrated in Figure 4, aims to distinguish between Moroccan Dialect and MSA using two different methods. Indeed, the first is rule-based and relies on stop word frequency, while the second is statically-based and is based on an SVM machine learning classifier.…”

Section: Web Applicationmentioning

confidence: 99%

A description and demonstration of SAFAR framework

Bouzoubaa¹,

Jaafar²,

Namly³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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Several tools and resources have been developed to deal with Arabic NLP. However, a homogenous and flexible Arabic environment that gathers these components is rarely available. In this perspective, we introduce SAFAR which is a monolingual framework developed in accordance with software engineering requirements and dedicated to Arabic language, especially, the modern standard Arabic and Moroccan dialect. After one decade of integration and development, SAFAR possesses today more than 50 tools and resources that can be exploited either using its API or using its web interface.3 https://whatis.techtarget.com/ 4 http://arabic.emi.ac.ma/safar

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“…In order to detect MA comments, we used the Language Identi¯cation (LID) system built by the authors in Ref. 16 by extending it to Arabizi. After that, we used the standalone language identi¯cation system \langid.py" 17 in order to distinguish between French, English and Spanish in the remaining comments.…”

Section: Classi¯cationmentioning

confidence: 99%

“…We enhanced this data with texts collected from Moroccan websites and blogs e to reach 3.6 million words. As it has been done for the UGT, we used our Moroccan Language Iden-ti¯cation system 16 in order to ensure that additional texts are written in Moroccan Arabic. Regarding the training, it is performed by optimizing the following loss function (Lo):…”

Section: Data and Model Trainingmentioning

confidence: 99%

Moroccan Data-Driven Spelling Normalization Using Character Neural Embedding

Tachicart

Bouzoubaa

2020

Vietnam J. Comp. Sci.

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With the increase of Web use in Morocco today, Internet has become an important source of information. Specifically, across social media, the Moroccan people use several languages in their communication leaving behind unstructured user-generated text (UGT) that presents several opportunities for Natural Language Processing. Among the languages found in this data, Moroccan Arabic (MA) stands with an important content and several features. In this paper, we investigate online written text generated by Moroccan users in social media with an emphasis on Moroccan Arabic. For this purpose, we follow several steps, using some tools such as a language identification system, in order to conduct a deep study of this data. The most interesting findings that have emerged are the use of code-switching, multi-script and low amount of words in the Moroccan UGT. Moreover, we used the investigated data in order to build a new Moroccan language resource. The latter consists in building a Moroccan words orthographic variants lexicon following an unsupervised approach and using character neural embedding. This lexicon can be useful for several NLP tasks such as spelling normalization.

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Automatic Identification of Moroccan Colloquial Arabic

Cited by 9 publications

References 7 publications

Arabic natural language processing: An overview

Arabic natural language processing: An overview

A description and demonstration of SAFAR framework

Moroccan Data-Driven Spelling Normalization Using Character Neural Embedding

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