State-of-the-art natural language processing (NLP) models are trained on massive training
corpora, and report a superlative performance on evaluation datasets. This survey delves …

We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool
that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and …

Arabic is a morphologically rich and complex language, with numerous dialectal variants.
Previous efforts on Arabic morphology modeling focused on specific variants and specific …

This paper presents a new corpus for a dialect of Arabic spoken in the central region of
Saudi Arabia: the Najdi Arabic Corpus. This is the first publicly available corpus for this …

This paper describes our winning systems in MRL: The 1st Shared Task on Multilingual
Clause-level Morphology (EMNLP 2022 Workshop) designed by KUIS AI NLP team. We …

We introduce Alma (), an open-source and state-of-the-art lemmatizer, POS tagger, and root
tagger for Arabic, boasting both high speed and accuracy. Alma relies on a dictionary of …

Automatic readability assessment is relevant to building NLP applications for education,
content analysis, and accessibility. However, Arabic readability assessment is a challenging …

Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling
challenges that have not been consistently addressed previously. This paper attempts to …

Building a system for morphological processing is a challenging task in morphologically
complex languages like Arabic. Although there are some deep learning based models that …

Grammatical error correction (GEC) is a well-explored problem in English with many existing
models and datasets. However, research on GEC in morphologically rich languages has …