Morphologically Annotated Corpora for Seven Arabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan

Alshargi, Faisal; Dibas, Shahd; Alkhereyf, Sakhar; Faraj, Reem; Abdulkareem, Basmah; Yagi, Sane; Kacha, Ouafaa; Habash, Nizar; Rambow, Owen

doi:10.18653/v1/w19-4615

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…NLP has several difficulties when working with dialectal Arabic. Arabic has a rich temporal affixal and inflectional morphology with several classes of attachable clitics (Al-Shargi et al, 2016). Arabic is the official language and is widely spoken in Yemeni official institutions (Al-Hamzi, 2021).…”

Section: The Yemeni Linguistic Situationmentioning

confidence: 99%

Expanding The Lexicon in Yemeni Arabic: A Study of (Non)Morphological Arabic Language

Al-Hamzi¹

2023

Preprint

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(Non)morphological word generation processes are one way to expand a language's lexicon. In this regard, the current study's specific goal is to explore the (non)morphological processes that Yemeni Urban Arabic (YUA) and Yemeni Rural Arabic (YRA) native speakers utilize to broaden their lexicons. The qualitative descriptive technique design was used to describe the data obtained in this study. The data was gathered from six individuals (4 males and 2 females) who are native speakers of the YA language. Using a high-fidelity digital recorder that was both tiny and lightweight, the data was recorded and saved. Respondents of YA native speakers (4 males and 2 females) were questioned in an interview to get the data. Specifically, the data set is comprised of transcriptions of recorded tokens that have been classified based on the (non)morphological processes that they have through. In this study, the researcher discovered a variety of (non)morphological processes that native speakers of Yemeni Urban Arabic and Yemeni Rural Arabic employ to broaden their lexicon. Morphological processes include suppletion, affixation, vowel mark placement, diminution, and conversion whereas univerbation, borrowing, and word creation are some of the non-morphological processes investigated in this study. Thus, the present research is focused on the (non)morphological mechanisms utilized to extend the YUA and YRA lexicons, respectively. This study's implications were also explained. So further research is required to identify how speakers of YBA (nomads) expand their linguistic repertoire.

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Section: The Yemeni Linguistic Situationmentioning

confidence: 99%

Expanding The Lexicon in Yemeni Arabic: A Study of (Non)Morphological Arabic Language

Al-Hamzi¹

2023

Preprint

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“…A corpus of 200K tokens was morphologically annotated covering seven different Arabic dialects including Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi, and Moroccan (Alshargi et al, 2019). The GUMAR Emirati corpus consists of 200K tokens collected from novels.…”

Section: Dialectal Arabic Resourcesmentioning

confidence: 99%

“…Dialectal Arabic (DA) content dominates informal writings in emails, social media, blogs, and social messaging. Interest in building computational resources for Arabic dialects has been in the rise to provide both (i) annotated corpora (Jarrar et al, 2022b;Alshargi et al, 2019;Bouamor et al, 2018;Jarrar et al, 2017;Al-Shargi et al, 2016;Zribi et al, 2015;Jarrar et al, 2014) and (ii) morphological dialect analyzers (Obeid et al, 2020;Pasha et al, 2014;Zribi et al, 2017;Abdul-Mageed et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Nâbra: Syrian Arabic Dialects with Morphological Annotations

Nayouf,

Hammouda,

Jarrar

et al. 2023

Proceedings of ArabicNLP 2023

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This paper presents Nâbra ( ), a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nâbra. Nâbra covers several local Syrian dialects including those of Aleppo, Damascus, Deir-ezzur, Hama, Homs, Huran, Latakia, Mardin, Raqqah, and Suwayda. A team of nine annotators annotated the 60K tokens with full morphological annotations across sentence contexts. We trained the annotators to follow methodological annotation guidelines to ensure unique morpheme annotations, and normalized the annotations. F1 and κ agreement scores ranged between 74% and 98% across features, showing the excellent quality of Nâbra annotations. Our corpora are open-source and publicly available as part of the Currasat portal https://sina.birzeit.edu/currasat.

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“…Al-Shargi et al . (2016) presented morphologically annotated corpora for Moroccan and Sanaani Yemeni Arabic. The corpora data were collected from both online and print materials such as internet comments, forums, oral interviews, folktales, sermons, textbooks, blogs and Facebook posts.…”

Section: Nlp Resources For Arabic Dialectsmentioning

confidence: 99%

Creation of annotated country-level dialectal Arabic resources: An unsupervised approach

Althobaiti

2021

Nat. Lang. Eng.

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The wide usage of multiple spoken Arabic dialects on social networking sites stimulates increasing interest in Natural Language Processing (NLP) for dialectal Arabic (DA). Arabic dialects represent true linguistic diversity and differ from modern standard Arabic (MSA). In fact, the complexity and variety of these dialects make it insufficient to build one NLP system that is suitable for all of them. In comparison with MSA, the available datasets for various dialects are generally limited in terms of size, genre and scope. In this article, we present a novel approach that automatically develops an annotated country-level dialectal Arabic corpus and builds lists of words that encompass 15 Arabic dialects. The algorithm uses an iterative procedure consisting of two main components: automatic creation of lists for dialectal words and automatic creation of annotated Arabic dialect identification corpus. To our knowledge, our study is the first of its kind to examine and analyse the poor performance of the MSA part-of-speech tagger on dialectal Arabic contents and to exploit that in order to extract the dialectal words. The pointwise mutual information association measure and the geographical frequency of word occurrence online are used to classify dialectal words. The annotated dialectal Arabic corpus (Twt15DA), built using our algorithm, is collected from Twitter and consists of 311,785 tweets containing 3,858,459 words in total. We randomly selected a sample of 75 tweets per country, 1125 tweets in total, and conducted a manual dialect identification task by native speakers. The results show an average inter-annotator agreement score equal to 64%, which reflects satisfactory agreement considering the overlapping features of the 15 Arabic dialects.

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Morphologically Annotated Corpora for Seven Arabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan

Cited by 13 publications

References 9 publications

Expanding The Lexicon in Yemeni Arabic: A Study of (Non)Morphological Arabic Language

Expanding The Lexicon in Yemeni Arabic: A Study of (Non)Morphological Arabic Language

Nâbra: Syrian Arabic Dialects with Morphological Annotations

Creation of annotated country-level dialectal Arabic resources: An unsupervised approach

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