REVIEW article
Front. Artif. Intell.
Sec. Natural Language Processing
Volume 8 - 2025 | doi: 10.3389/frai.2025.1638743
This article is part of the Research TopicEmerging Techniques in Arabic Natural Language ProcessingView all 5 articles
A Comparative Study of Arabic Syntactic Analyzers
Provisionally accepted
- 1Paragraphe research Lab, Universite Paris 8, Saint-Denis, France
- 2American University of Bahrain, Riffa, Bahrain
- 3Faculty of Technology, Universite Libanaise, Beirut, Lebanon
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Syntactic analysis stands at the heart of Natural Language Processing (NLP), serving as the cornerstone upon which deeper linguistic understanding is built-particularly for morphologically complex languages such as Arabic. This paper delivers a comprehensive comparative study of contemporary syntactic analyzers designed explicitly for Arabic, dissecting the strengths and limitations of rule-based, statistical, machine learning, and hybrid methodologies, and recent neural network and transformer-based models. Given Arabic's intricate morphological structure and rich syntactic variation, accurately capturing syntactic relationships poses a significant challenge. To address this complexity, our study meticulously evaluates existing algorithms, highlighting advancements, performance gaps, and practical trade-offs. In addition, recognizing that robust syntactic parsing is anchored in high-quality annotated datasets, we provide a thorough overview of available Arabic treebanks and annotated corpora, emphasizing their critical role and contribution to syntactic parsing advancements. By synthesizing current efforts in the domain, this comparative analysis not only offers clarity on the state-of-the-art but also guides future research directions. Ultimately, our work seeks to empower NLP practitioners and researchers with nuanced insights, enabling more informed choices in the development of powerful, accurate, and linguistically insightful Arabic syntactic analyzers.
Keywords: Arabic NLP, Arabic treebank, Syntactic analysis, Rule-based parsing, statistical parsing, Hybrid parsing, Neural Parsing, Transformer models
Received: 31 May 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Saadiyeh, Ramadan, Hajjar and Bernard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alaaeddine Ramadan, American University of Bahrain, Riffa, Bahrain
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