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Samhaa R. El-Beltagy

Samhaa R. El-Beltagy has 6 papers in our library, in 2016–2024, most of them on Semitic languages, Sentiment & emotion, Multilingual & cross-lingual NLP; often with Abu Bakr Soliman, Amr Keleg, Mahmoud Khalil.

6 papers in our library · 2016–2024

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📄 6 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #5914

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

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 6 worksArtificial Intelligence · 6 worksArts and Humanities · 5 worksLanguage and Linguistics · 5 worksInformation Systems · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 4Research subjects and languages the work is about.Semitic languages · 6 worksSentiment & emotion · 4 worksInformation retrieval & search · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20241 paper20221 paper20201 paper

2010s

20171 paper20162 papers

 Papers

The most cited first (OpenAlex and our library).

  1. NileULex: A Phrase and Word Level Sentiment Lexicon for Egyptian and Modern Standard Arabic (2016 · 65 citations)
    Arabic social media is mostly written in colloquial dialects, where sentiment is often carried by multiword idioms.
  2. NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis (2017 · 61 citations)
    The paper describes the NileTMRG entries for the Arabic subtasks of SemEval-2017 Task 4. These are three-way tweet polarity (A), two-way polarity toward a given topic (B) and estimating the positive/negative distribution of tweets about a topic (D).
  3. Exploring Retrieval Augmented Generation in Arabic (2024 · 22 citations)
    Retrieval-augmented generation has been studied mostly for English. It is unclear how well current embedding models and LLMs handle Arabic, which has rich morphology and many dialects that differ sharply from MSA. The authors build a complete Arabic RAG pipeline and evaluate it in two steps. First they compare…
  4. NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms (2016 · 12 citations)
    This paper describes the NileTMRG entry to the Arabic part of SemEval-2016 Task 7, where each Arabic Twitter term must receive a score showing how strongly it signals positive sentiment.
  5. ASU_OPTO at OSACT4 - Offensive Language Detection for Arabic text (2020 · 9 citations)
    The paper describes the ASU_OPTO team's entry to the OSACT4 shared task on Arabic tweets. Subtask A asks whether a tweet is offensive in any way. Subtask B asks whether it contains hate speech aimed at a person or group.
  6. NGU CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic (2022 · 6 citations)
    The paper describes the NGU_CNLP team's entry to the WANLP 2022 shared task on detecting propaganda in Arabic tweets.

 Co-authors here

4 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 37 times (of the 149,439 papers whose bibliographies we read); 32 citing papers with a page here →.

Updated

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