🧑‍🔬 Mourad Gridach

🏛️ Affiliations

🏢IQVIALondon · United Kingdom2026 · 1 papercurrent🎓Université Ibn ZohrAgadir · Morocco2017–2021 · 8 papers🎓University of OxfordOxford · United Kingdom2020–2024 · 4 papers🎓University of Colorado BoulderBoulder · United States2016–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

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

📅 Works by year

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media (2016 · 41 citations)
   The paper addresses named entity recognition (Person, Location, Organisation) for Arabic tweets. This is hard for three reasons: the language has rich morphology, it has no capitalisation cues, and social media mixes Dialectal Arabic with MSA. Earlier Arabic NER systems relied on large gazetteers and hand-built…
2. Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets (2017 · 25 citations)
   The paper describes the Tw-StAR team's entry to SemEval-2017 subtask 4A-Arabic, which asks systems to label each Arabic tweet as positive, negative or neutral.
3. Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects (2019 · 13 citations)
   Arabic dialects vary in syntax and allow free word order. A sentiment classifier that relies on ordered, syntax-aware sentence composition, such as a CNN or RecNN, may therefore work well for one dialect and poorly for another. Mulki et al. address this with Tw-StAR, a shallow feed-forward network that learns n-gram…
4. Developing a New System for Arabic Morphological Analysis and Generation (2011 · 10 citations)
   The paper addresses computational morphology for Modern Standard Arabic. Arabic builds words by applying schemes (patterns) to consonantal roots and then attaching prefixes and suffixes, which makes analysis hard. The authors first encode an Arabic morphological database in XMODEL, an XML-based Morphological…
5. Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis (2019 · 6 citations)
   The paper asks whether named entities (people, places, organizations) can serve as sentiment signals in Arabic social-media sentiment analysis, rather than being ignored or stripped out.
6. Developing a New Approach for Arabic Morphological Analysis and Generation (2011 · 6 citations)
   The paper describes AMAUT (Arabic Morphological Automaton), a system for both analysing and generating Arabic words.

🤝 Co-authors here

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

• Hala Mulki ×3
• Hatem Haddad ×3
• Ismail Babaoglu ×3
• Noureddine Chenfour ×2

Venues

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

• International Conference on Computational Linguistics (COLING): COLING 2016
• International Workshop on Semantic Evaluation (SemEval): SemEval 2017
• The International Arab Journal of Information Technology: The International Arab Journal of Information Technology 2019
• Arabic Natural Language Processing Workshop (WANLP): WANLP 2019
• Proceedings of the 2nd Workshop on South Southeast Asian Natural Language Processing (WSSANLP)

🔗 Cited by

Papers of our library cite their works 15 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated 31 October 2026

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