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Fahd Alotaibi

Fahd Alotaibi has 3 papers in our library, in 2012–2014, most of them on Semitic languages, Named entity recognition; often with Mark Lee.

3 papers in our library · 2012–2014

OpenAlex · ORCID · Wikidata · h-index 15 · 65 works · 963 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 16 times in our library · ⭐ Paper Stars ranking: #7679

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

🎓King Abdulaziz UniversityJeddah · Saudi Arabia2017–2025 · 48 paperscurrent🎓University of BirminghamBirmingham · United Kingdom2012–2014 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 3 worksNamed entity recognition · 2 works

 Works by year

2010s

20141 paper20131 paper20121 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mapping Arabic Wikipedia into the Named Entities Taxonomy (2012 · 21 citations)
    The paper addresses a gap in Arabic named-entity resources: supervised NER needs labelled data beyond newswire, and Arabic Wikipedia (AW) could supply it if each article were linked to an entity type.
  2. A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition (2014 · 20 citations)
    Most Arabic NER work tags only a few coarse types, such as person, organisation and location, in news text. This limits downstream uses like question answering. The authors address fine-grained Arabic NER with 50 classes grouped under 8 coarse ones.
  3. Automatically Developing a Fine-grained Arabic Named Entity Corpus and Gazetteer by utilizing Wikipedia (2013 · 14 citations)
    Arabic named entity recognition has mostly used a few coarse classes, and fine-grained annotated data is expensive to create by hand.

 Co-authors here

1 co-author 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 16 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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