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A complete, multi-layered quranic treebank dataset with hybrid syntactic annotations for classical arabic processing.

Wadee A Nashir1, Abdulqader M Mohsen2, Asma A Al-Shargabi3,1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, University of Science and Technology, Yemen.

Data in Brief
|August 21, 2025
PubMed
Summary

The Extended Quranic Treebank (EQTB) is a new linguistic resource for Classical Arabic (CA). It offers enhanced annotations and a full syntactic layer for NLP tasks and linguistic research.

Keywords:
Constituency ParsingDependency ParsingHoly quranLinguistic resourcesMorphological analysisSyntactic analysisTreebank

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Digital Humanities

Background:

  • Existing Quranic Treebank has limitations.
  • Need for a comprehensive, multi-layered linguistic resource for Classical Arabic (CA).

Purpose of the Study:

  • To describe the Extended Quranic Treebank (EQTB).
  • To overcome limitations of the original Quranic Treebank.
  • To provide a computationally accessible resource for CA.

Main Methods:

  • Leveraged foundational data from established Quranic digital resources.
  • Systematically expanded orthographic representations via algorithmic processing and validation.
  • Refined morphological annotations using expanded schemas, automated re-annotation, and manual curation.
  • Constructed a novel, complete syntactic layer using algorithmic conversion, Deep Learning-based parsing (hybrid constituency-dependency), and expert validation.

Main Results:

  • Developed EQTB encompassing the entire Quran (~132,736 tokens).
  • Structured data in adapted CoNLL-X format (43 columns).
  • Detailed multiple orthographies, fine-grained morphology (45 tags), and complete hybrid syntax (140 tags/labels).
  • Included auxiliary lexicons and schemas.

Conclusions:

  • EQTB offers significant reuse potential for CA NLP tasks (parsing, morphology, diacritization).
  • Provides crucial training/evaluation data for NLP.
  • Supports linguistic research and development of pedagogical tools and language technologies.