--- title: "Nizar Habash — papers, page 3 · 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/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/nizar-habash/page/3/) [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 3 # Nizar Habash · page 3 181 papers in our library · 2000–2026 · page 3 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/2/) [1](https://md.research.elamur.ai/free/en/authors/nizar-habash/) [2](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/4/) … [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/4/) 51. **[Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script](https://md.research.elamur.ai/free/en/papers/eskander-2014-foreign-words-automatic-processing-arabic/)** *(2014 · 70 citations)* Arabic on social media is often dialectal, has no fixed spelling, and is frequently typed in Latin letters ("Arabizi"). 52. **[Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/salloum-2013-dialectal-arabic-english-machine-translation/)** *(2013 · 68 citations)* Dialectal Arabic (DA) has no standard spelling and very few tools or parallel corpora. Modern Standard Arabic (MSA), by contrast, is well resourced, so MSA-trained translation systems often treat dialect words as unknown names. Salloum and Habash build ELISSA, a largely rule-based DA-to-MSA translator that serves as… 53. **[NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://md.research.elamur.ai/free/en/papers/mageed-2021-nadi-2021-second-nuanced-arabic/)** *(2021 · 67 citations)* NADI 2021 is the second round of a shared task on telling apart varieties of Arabic by where a tweet was written. 54. **[A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/)** *(2017 · 67 citations)* The paper looks at why sentiment analysis of Arabic tweets is hard and how well leading English opinion-mining models work when moved to Arabic. 55. **[Improving Arabic Dependency Parsing with Lexical and Inflectional Morphological Features](https://md.research.elamur.ai/free/en/papers/marton-2010-improving-arabic-dependency-parsing-lexical/)** *(2010 · 67 citations)* Marton, Habash and Rambow test which morphological and lexical features help a transition-based parser (MaltParser) on the Columbia Arabic Treebank (CATiB, converted from PATB part 3, gold segmentation). 56. **[Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules](https://md.research.elamur.ai/free/en/papers/biadsy-2009-improving-arabic-pronunciation-dictionary-phone/)** *(2009 · 67 citations)* The paper addresses a weak point in Modern Standard Arabic (MSA) speech recognition: the pronunciation dictionary. 57. **[A Large Scale Corpus of Gulf Arabic](https://md.research.elamur.ai/free/en/papers/khalifa-2016-large-scale-corpus-gulf-arabic/)** *(2016 · 64 citations)* Arabic NLP tools mostly target Modern Standard Arabic (MSA). Among the dialects, Egyptian has gained a fair set of resources, but Gulf Arabic (GA) has been left with very little. GA has no standard spelling and differs from MSA in sound and word structure. The authors address this gap with the Gumar Corpus. It holds… 58. **[Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon](https://md.research.elamur.ai/free/en/papers/diab-2014-tharwa-large-scale-dialectal-arabic/)** *(2014 · 62 citations)* Dialectal Arabic (DA), and Egyptian Arabic (EGY) in particular, is common in online text. Most Arabic NLP tools, however, are built for Modern Standard Arabic (MSA) and perform much worse on dialects. DA and MSA differ in phonology, morphology and vocabulary. Many written forms look the same across varieties but mean… 59. **[NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task](https://md.research.elamur.ai/free/en/papers/mageed-2024-nadi-2024-fifth-nuanced-arabic/)** *(2024 · 59 citations)* NADI 2024 is the fifth round of a shared-task series on Arabic dialect processing. It addresses a known weakness of earlier editions, which treated dialect identification as choosing exactly one country. Short texts from neighbouring regions are often equally natural in several dialects, and earlier analysis… 60. **[Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects](https://md.research.elamur.ai/free/en/papers/inoue-2022-morphosyntactic-tagging-pre-trained-language/)** *(2022 · 58 citations)* The paper asks how well fine-tuned pre-trained transformers handle full morphosyntactic tagging in Arabic. 61. **[Improving Arabic Diacritization through Syntactic Analysis](https://md.research.elamur.ai/free/en/papers/shahrour-2015-improving-arabic-diacritization-through-syntactic/)** *(2015 · 58 citations)* MADAMIRA chooses a morphological analysis from a ranked list for each word, but it looks only at a small window of neighbouring words and has no syntax. 62. **[Improving NER in Arabic Using a Morphological Tagger](https://md.research.elamur.ai/free/en/papers/farber-2008-improving-ner-arabic-morphological-tagger/)** *(2008 · 58 citations)* Arabic named entity recognition is harder than English: annotated data is scarcer, written words are highly ambiguous because short vowels are omitted and clitics are attached, and there is no capitalization to signal names. 63. **[Determining Case in Arabic: Learning Complex Linguistic Behavior Requires Complex Linguistic Features](https://md.research.elamur.ai/free/en/papers/habash-2007-determining-case-arabic-learning-complex/)** *(2007 · 57 citations)* The paper addresses automatic case assignment (nominative, accusative, genitive) for nouns and adjectives in Modern Standard Arabic. 64. **[Correction Annotation for Non-Native Arabic Texts: Guidelines and Corpus](https://md.research.elamur.ai/free/en/papers/zaghouani-2015-correction-annotation-non-native-arabic/)** *(2015 · 55 citations)* Learners of Arabic as a second language make errors that differ from those of native writers. Error-corrected L2 Arabic data, however, has been small, unannotated or not publicly available. The authors extend the QALB project, which built a large manually corrected corpus of native (L1) Arabic, to learner essays.… 65. **[An Arabic Morphological Analyzer and Generator with Copious Features](https://md.research.elamur.ai/free/en/papers/taji-2018-arabic-morphological-analyzer-generator-copious/)** *(2018 · 53 citations)* The paper presents CALIMA Star, an Arabic analyzer and generator built on the BAMA/SAMA/ALMOR family of pre-compiled six-table databases (three lexicon tables for prefixes, stems and suffixes, three compatibility tables). 66. **[Universal Dependencies for Arabic](https://md.research.elamur.ai/free/en/papers/taji-2017-universal-dependencies-arabic/)** *(2017 · 53 citations)* The paper presents NUDAR, a Universal Dependencies treebank for Modern Standard Arabic of 738,889 tokens in 19,738 sentences, made by converting the Penn Arabic Treebank (parts 1–3). 67. **[Morphological Analysis and Generation of Arabic Nouns: A Morphemic Functional Approach](https://md.research.elamur.ai/free/en/papers/altantawy-2010-morphological-analysis-generation-arabic-nouns/)** *(2010 · 49 citations)* MAGEAD is a morphological analyser and generator for Arabic built as finite-state transducers. It maps a lexeme and feature-value pairs to a surface form and back. This paper ports it from verbs to Modern Standard Arabic nominals, which are harder because of broken plurals and irregular feminines. A lexeme is a root… 68. **[Elissa: A Dialectal to Standard Arabic Machine Translation System](https://md.research.elamur.ai/free/en/papers/salloum-2012-elissa-dialectal-standard-arabic-machine/)** *(2012 · 48 citations)* Dialectal Arabic (DA), meaning the spoken and orthographically unstandardized varieties, has very few NLP resources. 69. **[CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation](https://md.research.elamur.ai/free/en/papers/shahrour-2016-camelparser-system-arabic-syntactic-analysis/)** *(2016 · 45 citations)* CamelParser is a public system that builds Arabic dependency trees (in the CATiB style) with the same morphological features that MADAMIRA produces, and then uses the syntax to correct MADAMIRA's choice of case and state. 70. **[Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging](https://md.research.elamur.ai/free/en/papers/zalmout-2020-joint-diacritization-lemmatization-normalization-fine/)** *(2020 · 44 citations)* Arabic lemmas and diacritized forms are open-vocabulary and best modelled on characters, while POS and the fourteen closed features are word-level tags. 71. **[Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic](https://md.research.elamur.ai/free/en/papers/shargi-2016-morphologically-annotated-corpora-morphological-analyzers/)** *(2016 · 44 citations)* Arabic dialects are now widely written online, but NLP tools mostly target Modern Standard Arabic (MSA) and Classical Arabic. 72. **[Noise-Robust Morphological Disambiguation for Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/zalmout-2018-noise-robust-morphological-disambiguation-dialectal/)** *(2018 · 43 citations)* The paper builds a morphological disambiguator for Egyptian Arabic, where spelling is unstandardised and most words have many analyses (about 24 per word, against about 12 in MSA). 73. **[YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer](https://md.research.elamur.ai/free/en/papers/khalifa-2016-yamama-yet-another-multi-dialect/)** *(2016 · 43 citations)* Arabic morphological processing is hard: words carry many inflections, the script is orthographically ambiguous (about a dozen full analyses per word on average), and dialects such as Egyptian (EGY) sit alongside Modern Standard Arabic (MSA). 74. **[A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/masmoudi-2014-corpus-phonetic-dictionary-tunisian-arabic/)** *(2014 · 42 citations)* Tunisian Arabic has almost no resources for automatic speech recognition. It also lacks a standard spelling. The authors address both gaps. First, they built TARIC (Tunisian Arabic Railway Interaction Corpus) from real conversations between clerks and customers at ticket offices of the Tunis railway station. The… 75. **[Automatic Error Type Annotation for Arabic](https://md.research.elamur.ai/free/en/papers/belkebir-2021-automatic-error-type-annotation-arabic/)** *(2021 · 41 citations)* Arabic grammatical error correction (GEC) has mostly been scored with the M2 Scorer. That tool reports a single number and does not say what kind of mistakes a system fixes or misses. The authors built ARETA, an error type annotator for Modern Standard Arabic inspired by the English tool ERRANT. It aligns two… [← prev](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/2/) [1](https://md.research.elamur.ai/free/en/authors/nizar-habash/) [2](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/nizar-habash/page/4/) … [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/4/)