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The SMarT Classifier for Arabic Fine-Grained Dialect Identification

Karima Meftouh, Karima Abidi, Salima Harrat, Kamel Smaili
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
This paper describes the approach adopted by the SMarT research group to build a dialect identification system in the framework of the Madar shared task on Arabic fine-grained dialect identification.  ...  Ngrams Conclusion In this paper, we described the experiments we conducted as part of the MADAR shared task on Arabic fine-grained dialect identification.  ...  This paper describes the submission of Loria (SMarT research group) to the Madar shared task on Arabic fine-grained dialect identification covering 25 specific cities from across the Arab World, in addition  ... 
doi:10.18653/v1/w19-4633 dblp:conf/wanlp/MeftouhAHS19 fatcat:p7c65ltkzbfsti4dmwypahqvze

The MADAR Shared Task on Arabic Fine-Grained Dialect Identification

Houda Bouamor, Sabit Hassan, Nizar Habash
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.  ...  The shared task includes two subtasks: the MADAR Travel Domain Dialect Identification subtask (Subtask 1) and the MADAR Twitter User Dialect Identification subtask (Subtask 2).  ...  In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.  ... 
doi:10.18653/v1/w19-4622 dblp:conf/wanlp/BouamorHH19 fatcat:pnm2ocv35bbidb726ojknnng2e

ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task.  ...  Conclusion In this paper, we proposed an Arabic fine-grained dialect identification system.  ...  In this paper, we describe a fine grained dialect identification systems that participated in MADAR 2019 Arabic Fine-Grained Dialect Identification task (Bouamor et al., 2019) In this task, our system  ... 
doi:10.18653/v1/w19-4635 dblp:conf/wanlp/AbbasLF19 fatcat:e5hfvzbegjcs3lgqy564tbjnyu

Simple But Not Naïve: Fine-Grained Arabic Dialect Identification Using Only N-Grams

Sohaila Eltanbouly, May Bashendy, Tamer Elsayed
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects  ...  Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies.  ...  Unlike most of the previous work which targeted coarse-grained Arabic dialect identification, this work presents the participation of Qatar University team in the Multi Arabic Dialect Applications and  ... 
doi:10.18653/v1/w19-4624 dblp:conf/wanlp/EltanboulyBE19 fatcat:p2ofcnyuvbeu7kn7h2mety6nhi

QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification

Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Mohammed Attia, Mohamed Eldesouki, Kareem Darwish
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification).  ...  In this paper, two resources created under the Multi-Arabic Dialect Applications and Resources (MADAR) project were used as the main resources for the task of Fine-Grained Dialect Identification .  ...  Arabic identification.  ... 
doi:10.18653/v1/w19-4639 dblp:conf/wanlp/SamihMAAED19 fatcat:isxq32pcwrgjzexay5j2fg7tyq

ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification

Kathrein Abu Kwaik, Motaz Saad
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
We build a coarse and a fine grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text.  ...  We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine grained model that classifies the sentence among 26  ...  The first model tries to predict the dialect among six different Arab dialects and known as coarse grained level, followed by the second model which goes much deeper and is known as a fine grained level  ... 
doi:10.18653/v1/w19-4632 dblp:conf/wanlp/KwaikS19 fatcat:bbo7z7ctkjahpgqdde4qxvxmym

Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification

Bashar Talafha, Ali Fadel, Mahmoud Al-Ayyoub, Yaser Jararweh, Mohammad AL-Smadi, Patrick Juola
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
In this paper, we describe our team's effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.  ...  The task requires building a system capable of differentiating between 25 different Arabic dialects in addition to MSA. Our approach is simple.  ...  (Sadat et al., 2014) dialects, a new task has been proposed for the fine-grained ADI focusing on a large number of city-/countrylevel dialects (Bouamor et al., 2019) .  ... 
doi:10.18653/v1/w19-4638 dblp:conf/wanlp/TalafhaFAJAJ19 fatcat:mtywlzelkfct3j2jqxtzlgqota

Automatic Identification Methods on a Corpus of Twenty Five Fine-Grained Arabic Dialects [chapter]

Salima Harrat, Karima Meftouh, Karima Abidi, Kamel Smaïli
2019 Communications in Computer and Information Science  
This research deals with Arabic dialect identification, a challenging issue related to Arabic NLP.  ...  Indeed, the increasing use of Arabic dialects in a written form especially in social media generates new needs in the area of Arabic dialect processing.  ...  The authors of [20] dealt with fine-grained dialect identification. They attempted to identify 25 dialects of different Arabic cities in addition to MSA.  ... 
doi:10.1007/978-3-030-32959-4_6 fatcat:qcbjxe2ldneixizdmwgjlzquiu

Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning

Ahmad Ragab, Haitham Seelawi, Mostafa Samir, Abdelrahman Mattar, Hesham Al-Bataineh, Mohammad Zaghloul, Ahmad Mustafa, Bashar Talafha, Abed Alhakim Freihat, Hussein Al-Natsheh
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1).  ...  We believe that a human benchmark might be useful for this fine-grained dialect detection problem, for which it would set a reasonable upper-bound that shows the significance of the orthographic features  ...  Dataset The dataset used for this shared task is the one provided by the Multi-Arabic Dialect Applications and Resources (MADAR). The task name is MADAR travel domain dialect identification task.  ... 
doi:10.18653/v1/w19-4630 dblp:conf/wanlp/RagabSSMAZMTFA19 fatcat:ewz3lillbve5fbrp5a347evvre

MASHAEIR: Bootstrapping a Multi-Dialect Fine- Grained Emotion Thesaurus for Arabic Using Twitter

Khaled Elghamry
2015 The Egyptian Journal of Language Engineering  
However, research on and resources for fine-grained emotion identification in Arabic texts are still lacking.  ...  To fill in this gap, this paper introduces MASHAEIR (an Arabic word that means 'emotions'), a corpus-based multi-dialect fine-grained emotion thesaurus for Arabic.  ...  Section 2 reviews previous work on the taxonomy and identification of fine-grained emotions.  ... 
doi:10.21608/ejle.2015.60192 fatcat:23qzk34d7jc7pmkcvsj27w2bfu

Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT [article]

Anshul Wadhawan
2021 arXiv   pre-print
This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI).  ...  The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from.  ...  The task targets dialects at the province-level, and also focuses on naturally-occurring fine-grained dialects at the sub-country level.  ... 
arXiv:2102.09749v2 fatcat:l636r4rh6jg3ldhnjgwrvw3wai

Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features

Youssef Fares, Zeyad El-Zanaty, Kareem Abdel-Salam, Muhammed Ezzeldin, Aliaa Mohamed, Karim El-Awaad, Marwan Torki
2019 Proceedings of the Fourth Arabic Natural Language Processing Workshop  
Among the important problems that should be explored is that of dialect identification.  ...  This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora.  ...  Fine grained or city-based Arabic dialect identification is the more challenging task of not only classifying dialect by country but also by city.  ... 
doi:10.18653/v1/w19-4626 dblp:conf/wanlp/FaresEAEMET19 fatcat:bur2abyj2veabb7bn5duttfarq

The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech

Ahmed Ali, Suwon Shon, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, James Glass, Steve Renals, Khalid Choukri
2019 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)  
The fine-grained Arabic dialect identification data was collected from known YouTube channels from 17 Arabic countries. 3,000 hours of this data was released for training, and 57 hours for development  ...  MGB-5 extends the previous MGB-3 challenge in two ways: first it focuses on Moroccan Arabic speech recognition; second the granularity of the Arabic dialect identification task is increased from 5 dialect  ...  The fine-grained Arabic Dialect Identification (ADI) task involved dialect identification of speech from YouTube across 17 dialects.  ... 
doi:10.1109/asru46091.2019.9003960 dblp:conf/asru/AliSSMAGRC19 fatcat:ozijlgh5lnhxvnkerpqzijacsa

Automatic Arabic Dialect Identification Systems for Written Texts: A Survey [article]

Maha J. Althobaiti
2020 arXiv   pre-print
Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text.  ...  Therefore, in the last decade, interest has increased in addressing the problem of Arabic dialect identification.  ...  [14] presented a DL method for Arabic fine-grained dialect identification.  ... 
arXiv:2009.12622v1 fatcat:ul32voarejenfdrfwsbxa46os4

Fine-grained analysis of language varieties and demographics

Francisco Rangel, Paolo Rosso, Wajdi Zaghouani, Anis Charfi
2020 Natural Language Engineering  
Furthermore, we applied our proposed method to a more fine-grained annotated corpus of Arabic varieties covering 22 Arab countries and obtained an overall accuracy of 88.89%.  ...  In this paper, we focus on a fine-grained analysis of language varieties while considering also the authors' demographics.  ...  In Section 5, we report on a more fine-grained Arabic language variety identification.  ... 
doi:10.1017/s1351324920000108 fatcat:mdk2yxafbjffnhm5te3b7lscxe
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