Quran Semantic Annotation Corpus with Multi-Label Tags

The Quran Semantic Annotation Corpus is a multi-label, semantically tagged corpus covering all verses of the Quran. It is designed for linguistic and natural language processing tasks involving religious text.

Use Cases

• Train a multi-label classifier using semantic tags to categorize Quranic verses by thematic content.
• Analyze the distribution and co-occurrence of semantic tags across all verses of the Quran.
• Build a semantic search engine for the Quran based on annotated tags to retrieve verses by conceptual meaning.
• Fine-tune an NLP model for Arabic text understanding using the provided semantic annotations as supervision.

Strengths

• Covers all verses of the Quran, providing complete textual coverage.
• Provides semantic annotations, enabling analysis beyond surface text.
• Designed for multi-label classification, reflecting complex thematic overlaps.

Limitations

• The specific annotation schema, tag set, and inter-annotator agreement metrics are not detailed.
• The corpus is static and focused solely on the Quran, limiting generalizability to other texts.
• The absence of column details and sample data prevents assessment of data structure and quality.

Provenance

Collection Method

Semantic annotation applied to the Quranic text.