We present BAREC-LexBench, a benchmark for Arabic lemma-level readability assessment, designed to evaluate readability lexicons and computational models independently of their granularity levels. We build the benchmark through a large-scale pairwise comparison task, where annotators determine which of two Arabic lemmas is easier to read and understand. The benchmark consists of 36,000 annotated pairs covering both same- and cross-part-of-speech comparisons among open-class lemmas: adjectives, nouns, proper nouns, and verbs. The annotations were produced by expert Arabic educators with an average inter-annotator agreement of 77.7% (0.56 Kappa ∼ moderate agreement), and we analyze disagreement patterns to better understand where lemma-level readability judgments are most challenging. We benchmark a wide range of approaches, from readability lexicons and frequency-based lexicons to trainable models and large language models (LLMs). Finally, we demonstrate downstream uses of the benchmark by constructing a ranked readability lexicon covering open-class lemmas (BAREC-LexRank) and evaluating its contribution to sentence-level readability assessment. To support research and education, we make the dataset, ranked lexicon, and benchmark results openly available.

Emirati Arabic remains one of the least-resourced Arabic dialects, with lexical information scattered across corpora, morphological databases, and dictionaries developed under incompatible annotation schemes. We present Gahwa, a machine-readable lexicon constructed by extracting, enriching, and standardizing evidence from an annotated Emirati corpus, a Gulf Arabic verb database, and a Gulf Arabic Wiktionary, unifying their input into one lemma-POS keyed scheme. We evaluate Gahwa through automatic coverage analysis of three Emirati datasets, GumarAE, Mixat, and the Ramsa Corpus, against existing Arabic morphological resources, and through analysis of out-of-vocabulary (OOV) forms. Results show that Gahwa has high word coverage across the three datasets, achieving 89.1-94.2% effective dediacritized lemma-POS token coverage, while the remaining OOV cases primarily reflect lexical gaps, orthographic variations, fused expressions, and morphological gaps.

Quranic Arabic poses important challenges for Arabic NLP due to its distinctive morphology, syntax, and orthography. We present MAQAM, a richly annotated Morphosyntactic Arabic Quranic Analysis and Modeling resource covering the entire Quran, providing 22 lexical, inflectional, and clitic features for every word. The resource is constructed through a semi-automatic pipeline combining feature enrichment, morphological synchronization, and expert manual annotation and validation. We further evaluate Quranic morphosyntactic disambiguation under a realistic setting where the input may be represented in either the Uthmani or Imla’i script, and investigate cross-domain transfer between Quranic Arabic and MSA. Our results show that MSA and Quranic resources are complementary for Quranic disambiguation, with the best performance obtained by combining MSA training data, Quran-specific analyzers, and both script representations, whereas for MSA, only integrating Quranic resources provide modest gains. We make MAQAM publicly available.

Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel approaches that frame lemmatization as classification into a Lemma-POS-Gloss (LPG) tagset, leveraging machine translation and semantic clustering. We also present a new Arabic lemmatization test set covering diverse genres, standardized alongside existing datasets. We evaluate character-level sequence-to-sequence models, which perform competitively and offer complementary value, but are limited to lemma prediction (not LPG) and prone to hallucinating implausible forms. Our results show that classification and clustering yield more robust, interpretable outputs, setting new benchmarks for Arabic lemmatization.

Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios.