NoorGhateh: A Benchmark Dataset for Training and Evaluating Arabic Morphological Analysis Systems

Noor-Ghateh: A Benchmark Dataset for Evaluating Arabic Word Segmentation Tools in the Hadith Domain

📘 Overview

Noor-Ghateh is a manually annotated Classical Arabic morphological dataset derived from the jurisprudential text Sharayeʿ al-Islam.The dataset provides fine-grained clitic segmentation, 15 morphological attributes, and gold-standard human annotation, making it a valuable benchmark for:

Morphological analyzers

Segmentation systems

Lemmatizers & root extractors

Classical Arabic NLP research

Benchmarking domain sensitivity across analyzers

The dataset includes 223,690 tokens, with a publicly available 313-token sample released in XML, JSON, and CSV-embedded-XML formats.

🧱 Data Format

1. XML Format (Primary)

The XML structure uses <Base> → <Root> → <word> hierarchy.Each <word> element includes 14 morphological attributes such as:

Seq — morpheme order

Slice — surface form

Affix — prefix/suffix/stem

Pos, Lemma, Case, Categ, DervT, Num, Root

TOV, Time, Voic, Kol, Lang

2. JSON Format

Direct JSON mapping of the XML hierarchy for machine learning pipelines.

3. CSV-embedded XML

Each row contains:Surface form — Segmented form — XML annotation block

🎯 Intended Use Cases

Training and evaluating morphological segmentation systems

Testing classical Arabic analyzers (Farasa, CAMeL Tools, ALP, MADAMIRA)

Building lemmatizers and root extractors

Domain-sensitivity analysis

Digital humanities research in Hadith & jurisprudence

Linguistic studies of Classical Arabic morphology