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A comparative study of Arabic syntactic analyzers.

Omar Saadiyeh1, Alaaeddine Ramadan2, Mohammad Hajjar3

  • 1Paragraphe Research Lab, University of Paris VIII, Paris, France.

Frontiers in Artificial Intelligence
|September 11, 2025
PubMed
Summary

This study compares Arabic syntactic analyzers, evaluating rule-based, machine learning, and neural network models. It highlights challenges in Arabic Natural Language Processing (NLP) and the importance of annotated datasets for advancing parsing techniques.

Keywords:
Arabic NLPArabic treebankhybrid parsingneural parsingrule-based parsingstatistical parsingsyntactic analysistransformer models

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing (NLP)
  • Artificial Intelligence

Background:

  • Syntactic analysis is crucial for Natural Language Processing (NLP), especially for morphologically rich languages like Arabic.
  • Arabic's complex morphology and syntactic variations present significant challenges for accurate parsing.

Purpose of the Study:

  • To conduct a comprehensive comparative study of contemporary Arabic syntactic analyzers.
  • To dissect the strengths, limitations, and trade-offs of various methodologies, including rule-based, statistical, machine learning, neural network, and transformer-based models.
  • To provide an overview of Arabic treebanks and annotated corpora vital for syntactic parsing advancements.

Main Methods:

  • Comparative analysis of existing Arabic syntactic parsing algorithms.
  • Evaluation of rule-based, statistical, machine learning, neural network, and transformer-based approaches.
  • Review of available Arabic treebanks and annotated corpora.

Main Results:

  • Identification of advancements and performance gaps in current Arabic syntactic analyzers.
  • Assessment of practical trade-offs among different parsing methodologies.
  • Emphasis on the critical role of high-quality annotated datasets in improving syntactic parsing.

Conclusions:

  • The study offers clarity on the state-of-the-art in Arabic syntactic analysis.
  • It provides insights to guide future research and development in NLP for Arabic.
  • Empowers NLP practitioners and researchers to make informed decisions for developing robust Arabic syntactic analyzers.