--- title: "Mona Diab — papers, page 3 · Carnegie Mellon University" description: "Mona Diab, Carnegie Mellon University: 84 papers on Elamur Research (2004–2026), h-index 48 — affiliations, topics, co-authors and who…" canonical: "https://research.elamur.ai/free/en/authors/mona-diab/page/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/mona-diab/page/3/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/) / page 3 # Mona Diab · page 3 84 papers in our library · 2004–2026 · page 3 of 4 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/mona-diab/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/mona-diab/page/2/) [1](https://md.research.elamur.ai/free/en/authors/mona-diab/) [2](https://md.research.elamur.ai/free/en/authors/mona-diab/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/mona-diab/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/mona-diab/page/4/) 51. **[CODACT: Towards Identifying Orthographic Variants in Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/dasigi-2011-codact-identifying-orthographic-variants-dialectal/)** *(2011 · 23 citations)* Dialectal Arabic (DA) is now widely written online, but it has no spelling standard. The same word can appear in many forms, and this makes data sparse for NLP tools built for Modern Standard Arabic (MSA). The authors present CODACT, an initial system that detects spelling variants in Arabic-script DA. They treat the… 52. **[An Unsupervised Approach for Bootstrapping Arabic Sense Tagging](https://md.research.elamur.ai/free/en/papers/diab-2004-unsupervised-approach-bootstrapping-arabic-sense/)** *(2004 · 21 citations)* Arabic had no word sense disambiguation (WSD) systems and almost no machine-readable lexical resources. Diab proposes SALAAM (Sense Annotations Leveraging Alignments And Multilinguality), an unsupervised method for bootstrapping Arabic sense tagging. It rests on two ideas. First, translation tends to split the senses… 53. **[A Framework for the Classification and Annotation of Multiword Expressions in Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/hawwari-2014-framework-classification-annotation-multiword-expressions/)** *(2014 · 20 citations)* The paper addresses the lack of lexical resources for multiword expressions (MWEs) in Arabic dialects. Social media and other user-generated content are full of such idioms, collocations and compounds, but existing MWE lexicons mostly cover edited or standard language. The authors present annotation guidelines for a… 54. **[Semi-automatic error analysis for large-scale statistical machine translation](https://md.research.elamur.ai/free/en/papers/kirchhoff-2007-semi-automatic-error-analysis-large/)** *(2007 · 19 citations)* The paper relates properties of the input documents to the translation quality of a large SMT system, scored per document by HTER. 55. **[Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data](https://md.research.elamur.ai/free/en/papers/diab-2016-creating-large-multi-layered-representational/)** *(2016 · 18 citations)* Code switching, where bilingual writers alternate between language varieties within a text or even within a word, causes NLP tools built for a single language to fail. 56. **[A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic](https://md.research.elamur.ai/free/en/papers/badrashiny-2017-layered-language-model-based-hybrid/)** *(2017 · 17 citations)* Arabic is normally written without most short-vowel and related marks, which leaves many words ambiguous for both readers and downstream tools such as POS tagging, text-to-speech and MT. 57. **[Building an Arabic Multiword Expressions Repository](https://md.research.elamur.ai/free/en/papers/hawwari-2012-building-arabic-multiword-expressions-repository/)** *(2012 · 17 citations)* The paper addresses the lack of a broad, morphologically annotated list of Arabic multiword expressions (MWEs). 58. **[Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-meta-ai-arabic-hate-speech/)** *(2022 · 13 citations)* The paper describes a submission to the OSACT 2022 shared task on fine-grained Arabic hate speech detection in tweets. 59. **[ASMA: A System for Automatic Segmentation and Morpho-Syntactic Disambiguation of Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/mageed-2013-asma-system-automatic-segmentation-morpho/)** *(2013 · 13 citations)* Arabic packs clitics, stems and inflectional affixes into a single written word, so POS tagging requires splitting words and disambiguating their morphology together. 60. **[Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data](https://md.research.elamur.ai/free/en/papers/alghamdi-2019-leveraging-pretrained-word-embeddings-part/)** *(2019 · 11 citations)* The paper studies part-of-speech tagging of text in which speakers mix two languages or dialects inside one sentence (intra-sentential code switching). 61. **[Robust Part-of-speech Tagging of Arabic Text](https://md.research.elamur.ai/free/en/papers/aldarmaki-2015-robust-part-speech-tagging-arabic/)** *(2015 · 11 citations)* The paper tackles part-of-speech tagging for Modern Standard Arabic. Its rich clitic morphology and the many words that never appear in training make simple taggers brittle. The authors start from MD-AMIRA, a fast cascaded pipeline inside the MADAMIRA suite. It runs three linear SVM steps in sequence: character-level… 62. **[Improvements To Monolingual English Word Sense Disambiguation](https://md.research.elamur.ai/free/en/papers/guo-2009-improvements-monolingual-english-word-sense/)** *(2009 · 11 citations)* The paper addresses all-words word sense disambiguation (WSD) in English: every content word in running text has to be mapped to its WordNet sense, and no sense-tagged training data is used by the disambiguator itself. 63. **[GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora](https://md.research.elamur.ai/free/en/papers/almarwani-2017-gw-qa-semeval-2017-task/)** *(2017 · 10 citations)* The paper addresses Subtask D of SemEval-2017 Task 3. Given a new question from an Arabic medical forum, a system must reorder a set of previously posted question–answer pairs so that the most related ones appear first. Arabic forum text is hard to process: the morphology is rich, word order is flexible, and posts… 64. **[Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition](https://md.research.elamur.ai/free/en/papers/tomeh-2013-reranking-linguistic-semantic-features-arabic/)** *(2013 · 10 citations)* Arabic OCR is hard because of connected letter shapes, diacritics and rich morphology. The BBN Byblos system recognises characters with an HMM and relies on an n-gram LM for fluency. The authors keep the decoder unchanged and instead rescore its n-best output. Each hypothesis receives features drawn from five families: 65. **[CUNIT: A Semantic Role Labeling System for Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/diab-2007-cunit-semantic-role-labeling-system/)** *(2007 · 10 citations)* The paper tackles semantic role labeling (SRL) for Modern Standard Arabic, a language that had no SRL system at the time. 66. **[Unsupervised Induction of Modern Standard Arabic Verb Classes](https://md.research.elamur.ai/free/en/papers/snider-2006-unsupervised-induction-modern-standard-arabic/)** *(2006 · 10 citations)* Modern Standard Arabic had no broad classification of verbs by alternation behavior in the style of Levin (1993). Snider and Diab try to induce such classes automatically from the Arabic Treebank (ATB). Their basis is the Levin Hypothesis: verbs that behave alike syntactically also share parts of their meaning. 67. **[Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics](https://md.research.elamur.ai/free/en/papers/liu-2025-synthetic-socratic-debates-examining-persona/)** *(2025 · 9 citations)* LLMs are often given personas and asked for advice on moral questions, yet earlier studies usually varied a single trait or relied on artificial dilemmas. 68. **[The Power of Language Music: Arabic Lemmatization through Patterns](https://md.research.elamur.ai/free/en/papers/attia-2016-power-language-music-arabic-lemmatization/)** *(2016 · 8 citations)* The paper tackles Arabic lemmatization, meaning recovering the dictionary citation form of a word, without any lookup lexicon or morphological analyzer. 69. **[DIRA: Dialectal Arabic Information Retrieval Assistant](https://md.research.elamur.ai/free/en/papers/pasha-2013-dira-dialectal-arabic-information-retrieval/)** *(2013 · 8 citations)* Searching Arabic text is difficult for two reasons. The language has rich morphology, and dialects such as Egyptian Arabic (EGY) differ from Modern Standard Arabic (MSA) in vocabulary, inflection and spelling. These differences matter more now that social media carries a lot of informal, unedited dialectal writing.… 70. **[Unsupervised Induction of Modern Standard Arabic Verb Classes Using Syntactic Frames and LSA](https://md.research.elamur.ai/free/en/papers/snider-2006-unsupervised-induction-modern-standard-arabic-p06-2102/)** *(2006 · 7 citations)* The paper addresses a gap: Modern Standard Arabic has no Levin-style classification grouping verbs by shared syntactic behaviour and, by the Levin Hypothesis, shared meaning components. 71. **[SAMER: A Semi-Automatically Created Lexical Resource for Arabic Verbal Multiword Expressions Tokens Paradigm and their Morphosyntactic Features](https://md.research.elamur.ai/free/en/papers/badrashiny-2016-samer-semi-automatically-created-lexical/)** *(2016 · 6 citations)* The paper builds SAMER, a lexical resource for Arabic verbal multiword expressions (AVMWE) in Modern Standard Arabic: verbal idioms, light-verb constructions and verb–particle constructions. 72. **[NLP for Arabic and Related Languages](https://md.research.elamur.ai/free/en/papers/diab-2017-nlp-arabic-related-languages/)** *(2017 · 5 citations)* This is the editors' preface to a 2017 special issue of the TAL journal on Arabic NLP. It reports no experiments of its own. It first explains why Arabic is hard for language technology. The language has heavy morphology and high ambiguity, and ambiguity grows because short vowels are usually left out in writing.… 73. **[Using Ambiguity Detection to Streamline Linguistic Annotation](https://md.research.elamur.ai/free/en/papers/zaghouani-2016-ambiguity-detection-streamline-linguistic-annotation/)** *(2016 · 4 citations)* Most written Arabic omits short vowels and other diacritics. This creates orthographic ambiguity on top of ordinary lexical ambiguity, and it makes building diacritized training corpora slow and error-prone. Within the OptDiac project, the authors test whether automatically flagging ambiguous words can make manual… 74. **[RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models](https://md.research.elamur.ai/free/en/papers/muhamed-2026-refusalbench-generative-evaluation-selective-refusal/)** *(2026 · 2 citations)* Retrieval-augmented systems need to recognize when the supplied documents cannot support a trustworthy answer and decline to respond. 75. **[Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic](https://md.research.elamur.ai/free/en/papers/hawwari-2016-explicit-fine-grained-syntactic-semantic/)** *(2016 · 2 citations)* In Arabic, Idafa is a broad construction that joins two nominal elements into one syntactic unit. It covers what English expresses as noun-noun compounds, 's-genitives and of-genitives, and it also appears in quantifier, quasi-preposition and adjectival patterns. The LDC Arabic Treebank (ATB) contains these… [← prev](https://md.research.elamur.ai/free/en/authors/mona-diab/page/2/) [1](https://md.research.elamur.ai/free/en/authors/mona-diab/) [2](https://md.research.elamur.ai/free/en/authors/mona-diab/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/mona-diab/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/mona-diab/page/4/)