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title: "Walid Magdy — papers, page 2 · University of Edinburgh"
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# Walid Magdy · page 2

32 papers in our library · 2006–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/walid-magdy/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Chandler: An Explainable Sarcastic Response Generator](https://md.research.elamur.ai/free/en/papers/oprea-2021-chandler-explainable-sarcastic-response-generator/)** *(2021 · 8 citations)* Earlier sarcasm generators assume that sarcasm simply means the opposite of what is said. The authors argue this assumption is neither necessary nor sufficient for sarcasm. Instead, they build Chandler on Utsumi's Implicit Display Theory (IDT). IDT treats an utterance as sarcastic exactly when three conditions hold.…
27. **[Revisiting Common Assumptions about Arabic Dialects in NLP](https://md.research.elamur.ai/free/en/papers/keleg-2025-revisiting-common-assumptions-about-arabic/)** *(2025 · 6 citations)* The paper tests four beliefs that Arabic NLP often treats as given:
28. **[Estimating the Level of Dialectness Predicts Inter-annotator Agreement in Multi-dialect Arabic Datasets](https://md.research.elamur.ai/free/en/papers/keleg-2024-estimating-level-dialectness-predicts-inter/)** *(2024 · 6 citations)* Multi-dialect Arabic datasets are usually labelled by native Arabic speakers assigned to samples at random.
29. **[Building a Heterogeneous Information Retrieval Collection of Printed Arabic Documents](https://md.research.elamur.ai/free/en/papers/abdelsapor-2006-building-heterogeneous-information-retrieval-collection/)** *(2006 · 3 citations)* The paper addresses a gap in Arabic search research. There was no large, realistic benchmark for retrieving Arabic text produced by OCR, which is often error-ridden. The authors drew on the Bibliotheca Alexandrina's part of the Million Book Project (75,000 Arabic books). They sampled about 25 pages from each of 1,378…
30. **[SMASH at Qur'an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering Scenarios](https://md.research.elamur.ai/free/en/papers/keleg-2022-smash-qur-qa-2022-creating/)** *(2022 · 2 citations)* The Qur'an QA 2022 shared task asks systems to pull answer spans out of Qur'anic passages written in Classical Arabic (CA), in response to questions posed in Modern Standard Arabic (MSA).
31. **[The NakbaEcho Dataset: From Oral Testimonies to a Transcribed Arabic History Corpus](https://md.research.elamur.ai/free/en/papers/balah-2026-nakbaecho-dataset-oral-testimonies-transcribed/)** *(2026)* Palestinian oral testimonies about the 1948 Nakba exist mostly as scattered audio and video recordings. Very little of this material is transcribed in a form that NLP or corpus research can use. Earlier computational work used small English-translated subsets or transcripts prepared for a single study. The authors…
32. **[Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets](https://md.research.elamur.ai/free/en/papers/keleg-2024-estimating-level-dialectness-predicts-interannotator/)** *(2024)* Multi-dialect Arabic datasets are usually labeled by native Arabic speakers who receive samples at random, with no regard to which dialect each speaker knows.

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