Nizar Habash
Nizar Habash researches Arabic natural language processing, including morphology, machine translation and dialect modeling. He earned a Maryland computer science PhD in 2003 and moved from Columbia to NYU Abu Dhabi in 2014. He coauthored the 2014 MADAMIRA system for Arabic morphological analysis and disambiguation. The shared 2020 CAMeL Tools paper describes an open Python toolkit for morphology, dialect identification, entities and sentiment. ACL elected him a fellow in 2025 for Arabic NLP, open resources and community work.
- Now at
- New York University
- Works
- 396
- Citations
- 11,879
- h-index
- 56
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2003–2014
A research career in language processing
Habash received his computer science PhD from the University of Maryland, College Park, in 2003. He researched at Columbia’s Center for Computational Learning Systems before joining NYU Abu Dhabi in 2014. His work includes machine translation, morphological analysis and Arabic dialects.
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2014
Combining Arabic analysis systems
Habash coauthored MADAMIRA with Pasha, Al-Badrashiny, Diab, El Kholy, Eskander, Pooleery, Rambow and Roth. The system combines aspects of the earlier MADA and AMIRA tools for Arabic morphological analysis and disambiguation. The paper describes a streamlined Java implementation and an online demonstration.
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2020
An open Python toolkit
With nine collaborators, Habash published CAMeL Tools, a collection of open tools for Arabic NLP. The paper covers preprocessing, morphological modeling, dialect identification, named-entity recognition and sentiment analysis.
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2025
Recognition for Arabic NLP
ACL elected Habash a fellow in 2025. The fellowship recognizes Habash’s work in Arabic NLP, open tools and resources for Arabic and other morphologically rich languages, and community building and service.
Sources and credits
Photo: Duaa Khidir, via Wikimedia Commons. License: CC BY-SA 4.0. Source: commons.wikimedia.org. Cropped to a square and resized. Ask us to remove or replace this photo.
