Language growth in child Emirati Arabic

Ntelitheos, Dimitrios; Idrissi, Ali

doi:10.1075/sal.5.10nte

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…[2], as cited in [3] norms for children between the ages of 3; 0-3; 5 shows total average of words 204.9 and total average of different words 92.5 TTR 0.45 while the mean score of the data collected from children between the ages of 2; 4 and 2; 9 is 140 and total average of different words 62.2 with TTR 0.447. In Emirati Arabic [17] found that there is no correlation between age and TTR was found in six children.…”

Section: Related Workmentioning

confidence: 89%

Lexical Growth in Egyptian Arabic Speaking Children: A corpus Based Study

Salama

Alansary

2017

The Egyptian Journal of Language Engineering

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This paper calculates developmental index of language growth in Egyptian Arabic based on a corpus consists of spontaneous speech samples from10 children 5 boys and 5 girls from 1.7 to 4 years. Depending on 30 minutes transcripts of spontaneous speech production the following properties of collected data were analysed: size of vocabulary and frequency of word use in relation to age (development of types and tokens) and individual differences in vocabulary size. The contribution of the current study lies in the use of vocabulary profile results as a measure of potential indicators of developmental language delay. The results provide a new measurement tool for lexical growth at different developmental stages.

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Section: Related Workmentioning

confidence: 89%

Lexical Growth in Egyptian Arabic Speaking Children: A corpus Based Study

Salama

Alansary

2017

The Egyptian Journal of Language Engineering

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“…The third corpus used in our experiments was the EMALAC corpus of Emirati Arabic child‐directed speech (Ntelitheos & Idrissi, ). The Emirati dialect of Arabic is the vernacular used in everyday interactions in the Gulf; like other dialects of Arabic, it is different than Modern Standard Arabic in a number of respects, including phonology, lexicon, and syntax.…”

Section: Methodsmentioning

confidence: 99%

Linguistic Constraints on Statistical Word Segmentation: The Role of Consonants in Arabic and English

Kastner

Adriaans

2017

Cognitive Science

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Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known about how a statistical learning mechanism copes with input regularities that arise from the structural properties of different languages. This study focuses on statistical word segmentation in Arabic, a Semitic language in which words are built around consonantal roots. We hypothesize that segmentation in such languages is facilitated by tracking consonant distributions independently from intervening vowels. Previous studies have shown that human learners can track consonant probabilities across intervening vowels in artificial languages, but it is unknown to what extent this ability would be beneficial in the segmentation of natural language. We assessed the performance of a Bayesian segmentation model on English and Arabic, comparing consonant-only representations with full representations. In addition, we examined to what extent structurally different proto-lexicons reflect adult language. The results suggest that for a child learning a Semitic language, separating consonants from vowels is beneficial for segmentation. These findings indicate that probabilistic models require appropriate linguistic representations in order to effectively meet the challenges of language acquisition.

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“…MADAR (Bouamor et al, 2014) is an ongoing multi-dialect corpora covering 26 different cities and their corresponding dialects. Other efforts cover Emirati (Ntelitheos and Idrissi, 2017;Khalifa et al, 2018), Tunisian and Algerian (Zribi et al, 2015;Harrat et al, 2014), andYemeni andMoroccan (Al-Shargi et al, 2016).…”

Section: Dialectal Resourcesmentioning

confidence: 99%

Curras + Baladi: Towards a Levantine Corpus

Haff¹,

Jarrar²,

Hammouda³

et al. 2022

Preprint

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This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus (Curras), and a newly annotated Lebanese corpus (Baladi). Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9.6K morphologically annotated tokens. Each token was manually annotated with several morphological features and using LDC's SAMA lemmas and tags. The inter-annotator evaluation on most features illustrates 78.5% Kappa and 90.1% F1-Score. Curras was revised by refining all annotations for accuracy, normalization and unification of POS tags, and linking with SAMA lemmas. This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects. Both corpora are publicly available through a web portal.

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Language growth in child Emirati Arabic

Cited by 11 publications

References 25 publications

Lexical Growth in Egyptian Arabic Speaking Children: A corpus Based Study

Lexical Growth in Egyptian Arabic Speaking Children: A corpus Based Study

Linguistic Constraints on Statistical Word Segmentation: The Role of Consonants in Arabic and English

Curras + Baladi: Towards a Levantine Corpus

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