--- title: "Ahmed Abdelali — papers, page 2 — Elamur Research" description: "Ahmed Abdelali, Qatar Science and Technology Park: 43 papers on Elamur Research (2008–2026), h-index 24 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/ahmed-abdelali/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/ahmed-abdelali/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Ahmed Abdelali](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) / page 2 # Ahmed Abdelali · page 2 43 papers in our library · 2008–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) [1](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) **2** 26. **[NatiQ: An End-to-end Text-to-Speech System for Arabic](https://md.research.elamur.ai/free/en/papers/abdelali-2022-natiq-end-end-text-speech/)** *(2022 · 16 citations)* The paper presents NatiQ, a web-deployed Arabic text-to-speech system. Arabic is a hard case for TTS because ordinary text leaves out short vowels, so the pronunciation has to be reconstructed before synthesis. The authors recorded two in-house corpora at 44kHz. "Amina" is an expressive female voice reading… 27. **[Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM](https://md.research.elamur.ai/free/en/papers/eldesouki-2017-arabic-multi-dialect-segmentation-bi/)** *(2017 · 16 citations)* The paper segments four Arabic dialects (Egyptian, Levantine, Gulf, Maghrebi) from raw Twitter text. Only about 350 manually segmented tweets per dialect are used for training, with 5-fold cross-validation and a 70/10/20 split. Two approaches are compared. An SVM ranker generates all segmentations of a word from a… 28. **[Bert Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets](https://md.research.elamur.ai/free/en/papers/harrag-2020-bert-transformer-model-detecting-arabic/)** *(2020 · 14 citations)* The paper tackles a classification problem for Arabic social media: telling apart tweets written by people from "deepfake" text produced by a neural language model. 29. **[QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification](https://md.research.elamur.ai/free/en/papers/samih-2019-qc-go-submission-madar-shared/)** *(2019 · 13 citations)* The paper reports the QC-GO team's entry in the MADAR Shared Task on fine-grained Arabic dialect identification. 30. **[QCRI@QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers' Errors](https://md.research.elamur.ai/free/en/papers/mubarak-2015-qcri-qalb-2015-shared-task/)** *(2015 · 12 citations)* The authors present the system QCRI entered in QALB-2015, a shared competition on automatically fixing errors in written Arabic. 31. **[AbjadGenEval: Abjad AI Generated Text Detection Shared Task for Languages Using Arabic Script at AbjadNLP 2026](https://md.research.elamur.ai/free/en/papers/ezzini-2026-abjadgeneval-abjad-ai-generated-text/)** *(2026 · 10 citations)* AbjadGenEval is a shared task held at the AbjadNLP workshop (EACL 2026). It tests whether systems can tell human-written news from LLM-generated news in languages written in Arabic script. The organizers argue that existing detectors were built mostly for English and break down on Arabic-script text, where rich… 32. **[The Effects of Language Relatedness on Multilingual Information Retrieval: A Case Study With Indo-European and Semitic Languages](https://md.research.elamur.ai/free/en/papers/chew-2008-effects-language-relatedness-multilingual-information/)** *(2008 · 10 citations)* The paper asks whether cross-language retrieval involving Semitic languages, especially Arabic, can be improved by changing only the training data while the retrieval model stays the same. 33. **[Post-hoc analysis of Arabic transformer models](https://md.research.elamur.ai/free/en/papers/abdelali-2022-post-hoc-analysis-arabic-transformer/)** *(2022 · 8 citations)* Arabic has many pre-trained transformers. They differ mainly in how much Modern Standard Arabic (MSA), dialectal and Classical text they were trained on. Earlier comparisons looked only at downstream scores. This paper instead inspects what the models encode internally. The authors study nine models: 34. **[Diacritization of Maghrebi Arabic Sub-Dialects](https://md.research.elamur.ai/free/en/papers/abdelali-2018-diacritization-maghrebi-arabic-sub-dialects/)** *(2018 · 6 citations)* Arabic is usually written without short vowels, and systems such as text-to-speech must restore them. Most diacritization research has targeted Modern Standard Arabic (MSA), so the authors address two Maghrebi sub-dialects, Moroccan (MOR) and Tunisian (TUN). Their data are two dialectal New Testament translations of… 35. **[Arabic Curriculum Analysis](https://md.research.elamur.ai/free/en/papers/mubarak-2020-arabic-curriculum-analysis/)** *(2020 · 5 citations)* Arabic has few NLP tools for checking whether school textbooks meet their learning goals, for example in the vocabulary they teach. 36. **[The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic](https://md.research.elamur.ai/free/en/papers/abdelali-2018-waw-corpus-first-corpus-interpreted/)** *(2018 · 5 citations)* Machine speech translation still trails human simultaneous interpreters, and no public interpreting corpus existed for Arabic. 37. **[Arabic Multimodal Machine Learning: Datasets, Applications, Approaches, and Challenges](https://md.research.elamur.ai/free/en/papers/haouhat-2025-arabic-multimodal-machine-learning-datasets/)** *(2025 · 3 citations)* This survey maps Arabic Multimodal Machine Learning (MML): systems that combine text, speech, images and video for tasks such as sentiment analysis, emotion recognition, retrieval and speech recognition. 38. **[Functional Text Dimensions for Arabic Text Classification](https://md.research.elamur.ai/free/en/papers/ferhat-2024-functional-text-dimensions-arabic-text/)** *(2024 · 3 citations)* Genre labels on web pages are often missing, vague or inconsistent between sources and annotators. This makes reliable genre classification hard, and Arabic lacks suitable evaluation data. The authors adapt Sharoff's Functional Text Dimensions (FTD) scheme to Arabic. In FTD, each category is defined by a diagnostic… 39. **[HUMAIN at IslamicEval 2025 Shared Task 1: A Three-Stage LLM-Based Pipeline for Detecting and Correcting Hallucinations in Quran and Hadith](https://md.research.elamur.ai/free/en/papers/omayrah-2025-humain-islamiceval-2025-shared-task/)** *(2025 · 2 citations)* The paper tackles a problem specific to religious content: LLMs often produce Quranic verses or Hadith that look plausible but are misquoted, misattributed or invented. 40. **[AraEval: An Arabic Multi-Task Evaluation Suite for Large Language Models](https://md.research.elamur.ai/free/en/papers/althnian-2025-araeval-arabic-multi-task-evaluation/)** *(2025 · 1 citation)* Most Arabic LLM evaluation is either translated from English benchmarks or narrowly focused on knowledge questions. 41. **[DialG2P: Dialectal Grapheme-to-Phoneme. Arabic as a Case Study](https://md.research.elamur.ai/free/en/papers/hawasly-2025-dialg2p-dialectal-grapheme-phoneme-arabic/)** *(2025 · 1 citation)* The paper addresses grapheme-to-phoneme (G2P) conversion for dialectal Arabic, using Egyptian (EGY) as the test case. 42. **[Performance Analysis of Arabic Pre-trained Models on Named Entity Recognition Task](https://md.research.elamur.ai/free/en/papers/dahou-2023-performance-analysis-arabic-pre-trained/)** *(2023 · 1 citation)* The paper looks at Arabic named entity recognition in dialectal text, specifically the Algerian dialect. That text has no capitalization cues, inconsistent spelling, frequent code-switching and Latin-script ARABIZI. Most earlier Arabic NER work targeted MSA, so the authors ask how BERT-style Arabic pre-trained models… 43. **[Automatic Expansion and Retargeting of Arabic Offensive Language Training](https://md.research.elamur.ai/free/en/papers/mubarak-2021-automatic-expansion-retargeting-arabic-offensive/)** *(2021)* The paper tackles a gap in Arabic offensive-language detection. Generic classifiers often miss abuse aimed at a particular entity, because such abuse can rely on deliberately twisted spellings of the target's name or on ordinary words used with hostile intent. The authors propose growing the training data… [← prev](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) [1](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/) **2**