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Shady Elbassuoni

Shady Elbassuoni has 7 papers in our library, in 2017–2026, most of them on Semitic languages, Evaluation & benchmarks, Named entity recognition; often with Chadi Helwe, Khaled Shaban, Wassim El-Hajj.

7 papers in our library · 2017–2026

OpenAlex · ORCID · Wikidata · h-index 23 · 111 works · 2,355 citations OpenAlex, 2026-10-14

📄 7 works in our library · 🔗 Cited 32 times in our library · ⭐ Paper Stars ranking: #6180

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓American University of BeirutBeirut · Lebanon2013–2026 · 61 papersEmployment since 2013 · Associate Professor, Computer Sciencecurrent🔬Max Planck Institute for InformaticsSaarbrücken · Germany2008–2012 · 13 papers🔬Max Planck SocietyMunich · Germany2008–2011 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 7 worksArtificial Intelligence · 7 worksArts and Humanities · 6 worksLanguage and Linguistics · 6 worksSocial Sciences · 4 worksSociology and Political Science · 3 worksInformation Systems · 2 worksEducation · 1 work

🧭 Topics 3Research subjects and languages the work is about.Semitic languages · 7 worksEvaluation & benchmarks · 2 worksNamed entity recognition · 1 work

 Works by year

2020s

20262 papers20211 paper20201 paper

2010s

20191 paper20172 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CAT: Credibility Analysis of Arabic Content on Twitter (2017 · 52 citations)
    The paper addresses automatic credibility judgment for individual Arabic tweets. Unreliable or spam posts degrade downstream tasks such as sentiment analysis and rumor detection, and Arabic had received little attention here. The authors collected about 17 million hashtagged Arabic tweets over two weeks and filtered…
  2. Methodical Evaluation of Arabic Word Embeddings (2017 · 47 citations)
    The paper tackles the lack of a proper way to judge the quality of Arabic word embeddings. Earlier work mostly machine-translated English analogy sets, which leads to errors, irrelevant questions, and missing Arabic-specific relations. The authors had an Arabic linguist define nine relation types, such as capital…
  3. A Semi-Supervised BERT Approach for Arabic Named Entity Recognition (2020 · 25 citations)
    The paper tackles Arabic named entity recognition (NER), which is hard for several reasons. Arabic script has no capital letters, many names double as ordinary nouns or adjectives, the morphology is rich, and annotated corpora are scarce. The authors restrict the task to person, location and organization names in BIO…
  4. Assessing Arabic Weblog Credibility via Deep Co-learning (2019 · 24 citations)
    The paper tackles automatic credibility assessment of Arabic blog posts when labeled data is very scarce. The only public resource is the crowdsourced corpus of Al Zaatari et al. (2016), with 268 posts. Each post is marked credible, fairly credible or not credible, and the posts cover topics such as regional wars and…
  5. DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings (2021 · 3 citations)
    Arabic dialects had no shared way to check whether word embeddings capture their vocabulary and grammar. Earlier analogy benchmarks for Arabic covered only Modern Standard Arabic (MSA) or were translated from English. DiaLex fills this gap. It is a hand-built set of word pairs for Algerian, Egyptian, Lebanese, Syrian…
  6. ARAFA: An LLM-Generated Arabic Fact-Checking Dataset (2026)
    Arabic automatic fact-checking has lacked a large, general-domain training resource. Earlier Arabic sets such as AraFacts and AraStance are small and mostly limited to news or posts on X. The authors build ARAFA, a Modern Standard Arabic dataset produced almost entirely by LLMs, using a three-stage pipeline:
  7. DIA2 - a Comprehensive and Diverse Diacritized Arabic Corpus for NLP Research (2026)
    Arabic NLP and Arabic LLMs lack large, clean, natively written training text. Existing big corpora often depend on noisy web crawls, include machine-translated material, skip systematic deduplication, or strip diacritics, even though diacritics carry grammatical and lexical distinctions that undiacritized script…

 Co-authors here

10 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 32 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

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