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 8 publications

(4 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%

“…Over the years, there has been a lot of descriptive work on the same subject. The number of theoretical and descriptive linguistic work on Yemeni Arabic was assessed by (Peter Behnstedt, 2017); (Jastrow, 1984); (Abu-Haidar, 1994); (Al-Shargi et al, 2016); (Naïm-Sanbar, 1994) and Behnstedt, 2006). Rubin (2018) examined the morphology of the Mehri Qishn dialect, one of the most widely-spoken dialects in Yemeni Arabic.…”

Section: Introductionmentioning

confidence: 99%

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

Al-Hamzi¹

2023

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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%

Section: Introductionmentioning

confidence: 99%

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

Al-Hamzi¹

2023

Preprint

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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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“…Some of the earlier efforts worked on rule-based approaches to model dialectal morphology directly (Habash and Rambow, 2006;Habash et al, 2012), or exploiting existing MSA resources (Salloum and Habash, 2014). Later, a number of annotation efforts have led to the creation of varying sizes of dialectal annotated corpora following the style of the PATB (Maamouri et al, 2014;Jarrar et al, 2016;Al-Shargi et al, 2016;Alshargi et al, 2019). The created annotations supported models for dialectal Arabic analysis, disambiguation and tokenization building on the same successful approaches in MSA (Eskander et al, 2016a;Habash et al, 2013;Pasha et al, 2014;Zalmout and Habash, 2019).…”

Section: Dialectal Arabic Models Work On Dialectalmentioning

confidence: 99%

A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

Erdmann¹,

Khalifa²,

Oudah³

et al. 2019

Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

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We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring language specific knowledge, but no direct supervision. Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours. The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form. Extending the grammar to cover orthographic, morphosyntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content. We demonstrate the utility of de-lexical segmentation on several dialects of Arabic. We consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.

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

Cited by 8 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

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

A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

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