--- title: "Younes Samih — papers, page 2 — Elamur Research" description: "Younes Samih, IBM: 29 papers on Elamur Research (2012–2026), h-index 18 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/younes-samih/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/younes-samih/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Younes Samih](https://md.research.elamur.ai/free/en/authors/younes-samih/) / page 2 # Younes Samih · page 2 29 papers in our library · 2012–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/younes-samih/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/younes-samih/) [1](https://md.research.elamur.ai/free/en/authors/younes-samih/) **2** 26. **[From Multiple-Choice to Extractive QA: A Case Study for English and Arabic](https://md.research.elamur.ai/free/en/papers/lynn-2025-multiple-choice-extractive-qa-case/)** *(2025 · 1 citation)* Annotated data for task-specific NLP is scarce outside a few major languages. The authors ask whether a dataset built for one QA format can be turned into another. They take part of BELEBELE, a multiple-choice reading-comprehension benchmark covering 122 language variants, and convert it into extractive QA (EQA)… 27. **[Conversion of Procedural Morphologies to Finite-State Morphologies: A Case Study of Arabic](https://md.research.elamur.ai/free/en/papers/hulden-2012-conversion-procedural-morphologies-finite-state/)** *(2012 · 1 citation)* The Buckwalter Arabic Morphological Analyzer is a Perl program that splits a word into prefix, stem and suffix, checks the pieces against three lexicons and three co-occurrence tables, and retries with spelling variants when lookup fails. 28. **[EDRAC: Benchmarking Arabic Dialect Reading Comprehension](https://md.research.elamur.ai/free/en/papers/mokh-2026-edrac-benchmarking-arabic-dialect-reading/)** *(2026)* Dialectal Arabic has far fewer resources than Modern Standard Arabic. Existing dialect reading-comprehension tests are almost all multiple-choice and built from translated or social-media text. EDRAC fills this gap with an open-ended, generative QA benchmark for five dialects: Egyptian, Moroccan, Emirati, Syrian and… 29. **[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/younes-samih/) [1](https://md.research.elamur.ai/free/en/authors/younes-samih/) **2**