--- title: "Nizar Habash — papers, page 7 · New York University Abu Dhabi" description: "Nizar Habash, New York University Abu Dhabi: 181 papers on Elamur Research (2000–2026), h-index 56 — affiliations, topics, co-authors…" canonical: "https://research.elamur.ai/free/en/authors/nizar-habash/page/7/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/nizar-habash/page/7/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) / page 7 # Nizar Habash · page 7 181 papers in our library · 2000–2026 · page 7 of 8 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/nizar-habash/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/6/) [1](https://md.research.elamur.ai/free/en/authors/nizar-habash/) … [6](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/6/) **7** [8](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/8/) [next →](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/8/) 151. **[Computational Morphology and Lexicography Modeling of Modern Standard Arabic Nominals](https://md.research.elamur.ai/free/en/papers/khairallah-2024-computational-morphology-lexicography-modeling-modern/)** *(2024 · 4 citations)* The paper models Modern Standard Arabic nouns, adjectives and elatives inside the CAMeL Morph framework (Habash et al., 2022), an open project that uses compatibility rules between allomorphs and an editable lexicon. 152. **[Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models](https://md.research.elamur.ai/free/en/papers/zalmout-2020-utilizing-subword-entities-character-level/)** *(2020 · 4 citations)* The paper addresses lemmatization of Arabic, where undiacritized words must be mapped to fully diacritized lemmas despite clitics and templatic morphology that make readings ambiguous. 153. **[The Shared Task on Gender Rewriting](https://md.research.elamur.ai/free/en/papers/alhafni-2022-shared-task-gender-rewriting/)** *(2022 · 3 citations)* This paper reports on a shared task, held at WANLP 2022, on gender rewriting for Modern Standard Arabic. The task takes a sentence and produces versions that fit each combination of speaker gender and listener gender. In Arabic, words that refer to the first or second person carry grammatical gender, so a system that… 154. **[Applying the Cognitive Machine Translation Evaluation Approach to Arabic](https://md.research.elamur.ai/free/en/papers/temnikova-2016-applying-cognitive-machine-translation-evaluation/)** *(2016 · 3 citations)* The paper asks whether a cognitively motivated scheme for judging how hard machine-translated text is to post-edit, first built for Russian, Spanish and Bulgarian, still works for a typologically distant language, Modern Standard Arabic. 155. **[SPMRL‘13 Shared Task System: The CADIM Arabic Dependency Parser](https://md.research.elamur.ai/free/en/papers/marton-2013-spmrl-13-shared-task-system/)** *(2013 · 3 citations)* This paper describes the Columbia Arabic & Dialect Modeling group (CADIM) entry to the SPMRL 2013 Shared Task. 156. **[A Large and Balanced Multi-Domain Arabic Corpus Annotated for Morphology, Syntax, and Readability](https://md.research.elamur.ai/free/en/papers/elmadani-2026-large-balanced-multi-domain-arabic/)** *(2026 · 2 citations)* Arabic corpora have usually concentrated on one dimension: syntactic and morphological annotation, broad domain coverage, or readability labels. 157. **[A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic](https://md.research.elamur.ai/free/en/papers/gonzalez-2026-tale-two-scripts-transliteration-post/)** *(2026 · 2 citations)* Judeo-Arabic, the medieval Arabic of Jewish communities, was written in Hebrew letters. As a result, Arabic readers cannot use these texts, and Arabic NLP tools, which expect Arabic script, cannot process them. Converting the script is difficult for several reasons: letters map ambiguously, the use of the upper dot… 158. **[Beyond Cairo: Sa'idi Egyptian Arabic Literary Corpus Construction and Analysis](https://md.research.elamur.ai/free/en/papers/eida-2025-beyond-cairo-sa-idi-egyptian/)** *(2025 · 2 citations)* Egyptian Arabic NLP has been built almost entirely around the Cairo variety. Sa'idi Egyptian Arabic (SEA), spoken by tens of millions in Upper Egypt, has no released text resources. Its speakers tend to suppress stigmatized dialect markers when writing online, so tweets are a poor source. The authors turn to… 159. **[Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://md.research.elamur.ai/free/en/papers/saeed-2025-lemmatization-as-classification-task-results/)** *(2025 · 2 citations)* Arabic lemmatization is hard because the orthography drops vowels, so one written word has several lemma, POS and gloss readings. 160. **[The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness](https://md.research.elamur.ai/free/en/papers/ban-2025-arabic-generality-score-another-dimension/)** *(2025 · 2 citations)* Arabic dialect NLP usually assigns texts discrete dialect labels. Newer work such as ALDi treats dialectness as a continuous value, but that value only measures how far text drifts from MSA. The authors add a second, complementary axis: the Arabic Generality Score (AGS). It rates, on a 0–1 scale, how widely a word is… 161. **[The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic](https://md.research.elamur.ai/free/en/papers/abdulrahim-2022-bahrain-corpus-multi-genre-corpus/)** *(2022 · 2 citations)* Most Arabic NLP resources cover Modern Standard Arabic. The few large Gulf Arabic collections, such as the Gumar Corpus, come almost entirely from one genre: conversational novels posted on online forums. This paper builds a dedicated, genre-diverse corpus of the Bahraini dialect. It contains about 620K words from… 162. **[DALILA: The Dialectal Arabic Linguistic Learning Assistant](https://md.research.elamur.ai/free/en/papers/khalifa-2016-dalila-dialectal-arabic-linguistic-learning/)** *(2016 · 2 citations)* Learners of Arabic who read real online text keep running into Dialectal Arabic (DA). Tools built for Modern Standard Arabic (MSA) handle it poorly, and general machine translators often fail on dialect words, sometimes just transliterating them. The authors built DALILA, a Google Chrome extension. A user… 163. **[A Bilingual Bimodal Benchmark for Arabic-English NLP across Grammatical Correction, Essay Scoring, Morphological Tagging, and Speech Recognition](https://md.research.elamur.ai/free/en/papers/alhafni-2026-bilingual-bimodal-benchmark-arabic-english/)** *(2026 · 1 citation)* The authors present ZAEBUC*, a single resource that brings together all previous corpora of the ZAEBUC project. 164. **[Arabic Sentence Segmentation Across Genres and Punctuation Conditions](https://md.research.elamur.ai/free/en/papers/elkholy-2026-arabic-sentence-segmentation-across-genres/)** *(2026 · 1 citation)* Arabic sentence boundaries are hard to find because punctuation is sparse, inconsistent or absent in many texts, and clauses are often joined by conjunctions such as wa- and fa-. 165. **[Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks](https://md.research.elamur.ai/free/en/papers/abouzahir-2026-cross-lingual-empirical-evaluation-large/)** *(2026 · 1 citation)* The paper investigates why large language models do worse on Arabic medical questions than on English ones. 166. **[A Derivational ChainBank for Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/marzouk-2025-derivational-chainbank-modern-standard-arabic/)** *(2025 · 1 citation)* The paper builds a derivational resource for Modern Standard Arabic: for each root, a tree that links every derived lemma (masdar, active and passive participles, nouns of place, adjectives in -iy, and so on) to its base, through a network of abstract patterns. 167. **[ARWI: Arabic Write and Improve](https://md.research.elamur.ai/free/en/papers/chirkunov-2025-arwi-arabic-write-improve/)** *(2025 · 1 citation)* Arabic has hundreds of millions of speakers, yet tools that help people write better Modern Standard Arabic (MSA) are scarce. Existing tools mostly fix frequent spelling slips and do not assess overall writing quality. ARWI is a free web-based writing assistant built to close this gap. It combines several parts: 168. **[BAREC Demo: Resources and Tools for Sentence-level Arabic Readability Assessment](https://md.research.elamur.ai/free/en/papers/altarbouch-2025-barec-demo-resources-tools-sentence/)** *(2025 · 1 citation)* Picking Arabic texts that fit a learner's level is difficult. The language has rich morphology, optional diacritics and flexible word order, and its standard and dialectal varieties coexist. Earlier Arabic readability work mostly scored whole documents or single words and covered few genres. This demo paper presents… 169. **[Evaluating Prompt Relevance in Arabic Automatic Essay Scoring: Insights from Synthetic and Real-World Data](https://md.research.elamur.ai/free/en/papers/qwaider-2025-evaluating-prompt-relevance-arabic-automatic/)** *(2025 · 1 citation)* The paper tackles a question Arabic essay scoring had not modelled explicitly: does a student's essay actually address the prompt it was written for? The authors treat this as binary on-topic/off-topic classification. 170. **[Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset](https://md.research.elamur.ai/free/en/papers/bondok-2025-proper-noun-diacritization-arabic-wikipedia/)** *(2025 · 1 citation)* Arabic script usually leaves out short-vowel and gemination marks. Proper nouns in Arabic Wikipedia therefore often have more than one reading, as with the same spelling for "Oman" and "Amman". Foreign names are the hardest case. The authors found that 99.45% of their entries carried no diacritics at all. They treat… 171. **[Arabic Dialect Processing Tutorial](https://md.research.elamur.ai/free/en/papers/diab-2012-arabic-dialect-processing-tutorial/)** *(2012 · 1 citation)* This is a tutorial abstract, not a research paper, so it reports no experiments, new data or measured results. 172. **[MT and Arabic Language Issues](https://md.research.elamur.ai/free/en/papers/habash-2012-mt-arabic-language-issues/)** *(2012 · 1 citation)* This AMTA 2012 tutorial by Nizar Habash surveys the main obstacles Arabic poses for machine translation and the practical remedies used around that time. It is aimed at MT researchers and translators and assumes no knowledge of Arabic. It is a review rather than a single experiment, organised by linguistic layer: 173. **[Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models](https://md.research.elamur.ai/free/en/papers/adel-2026-arabic-morphosyntactic-tagging-dependency-parsing/)** *(2026)* The authors test whether instruction-tuned large language models can produce explicit Arabic grammatical analysis: 14 morphological features per token (part of speech, aspect, mood, voice, person, gender, number, case, state, four proclitics and one enclitic), and labelled CATiB dependency trees. 174. **[ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation](https://md.research.elamur.ai/free/en/papers/elhady-2026-arabigee-hierarchical-taxonomy-arabic-grammatical/)** *(2026)* Explanations of Arabic grammar errors have so far been produced as free text. That makes them hard to score automatically, and older Arabic error taxonomies such as ALC are too flat to support them. The authors present ArabiGEE, a hand-built hierarchy for Arabic grammatical error explanation (GEE) that is organized… 175. **[Benchmarking Arabic Authorship Attribution and Style Transfer with Large Language Models](https://md.research.elamur.ai/free/en/papers/hamed-2026-benchmarking-arabic-authorship-attribution-style/)** *(2026)* The paper tackles two understudied problems for Arabic: identifying who wrote a text (authorship attribution, AA) and rewriting text in a given author's style (authorship style transfer). [← prev](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/6/) [1](https://md.research.elamur.ai/free/en/authors/nizar-habash/) … [6](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/6/) **7** [8](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/8/) [next →](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/8/)