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.

We present a collection of morphologically annotated corpora for seven Arabic dialects: Taizi Yemeni, Sanaani Yemeni, Najdi, Jordanian, Syrian, Iraqi and Moroccan Arabic. The corpora collectively cover over 200,000 words, and are all manually annotated in a common set of standards for orthography, diacritized lemmas, tokenization, morphological units and English glosses. These corpora will be publicly available to serve as benchmarks for training and evaluating systems for Arabic dialect morphological analysis and disambiguation.