Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed previously. This paper attempts to define the space of such challenges, and leverage a recently proposed morphological framework to build a comprehensive and extensible model for MSA nominals. Our model design addresses the nominals’ intricate morphotactics, as well as their paradigmatic irregularities. Our implementation showcases enhanced accuracy and consistency compared to a commonly used MSA morphological analyzer and generator. We make our models publicly available.

We present Camel Morph MSA, the largest open-source Modern Standard Arabic morphological analyzer and generator. Camel Morph MSA has over 100K lemmas, and includes rarely modeled morphological features of Modern Standard Arabic with Classical Arabic origins. Camel Morph MSA can produce ∼1.45B analyses and ∼535M unique diacritizations, almost an order of magnitude larger than SAMA (Maamouri et al., 2010c), in addition to having ∼36% less OOV rate than SAMA on a 10B word corpus. Furthermore, Camel Morph MSA fills the gaps of many lemma paradigms by modeling linguistic phenomena consistently. Camel Morph MSA seamlessly integrates with the Camel Tools Python toolkit (Obeid et al., 2020), ensuring ease of use and accessibility.

How well does naturally-occurring digital text, such as Tweets, represent sub-dialects of Egyptian Arabic (EA)? This paper focuses on two EA sub-dialects: Cairene Egyptian Arabic (CEA) and Sa’idi Egyptian Arabic (SEA). We use morphological markers from ground-truth dialect surveys as a distance measure across four geo-referenced datasets. Results show that CEA markers are prevalent as expected in CEA geo-referenced tweets, while SEA markers are limited across SEA geo-referenced tweets. SEA tweets instead show a prevalence of CEA markers and higher usage of Modern Standard Arabic. We conclude that corpora intended to represent sub-dialects of EA do not accurately represent sub-dialects outside of the Cairene variety. This finding calls into question the validity of relying on tweets alone to represent dialectal differences.