---
title: "MURAD: A Large-Scale Multi-Domain Unified — Sibaee 2026"
description: "Arabic has few large resources that pair words with formal definitions."
authors: ["Serry Sibaee", "Yasser Alhabashi", "Nadia Sibai", "Yara Farouk", "Adel Ammar", "Sawsan AlHalawani", "Wadii Boulila"]
year: 2026
venue: "arXiv:2601.21512"
source: "https://arxiv.org/abs/2601.21512"
doi: "10.48550/arxiv.2601.21512"
arxiv: "2601.21512"
licence: "CC BY-NC-ND 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sibaee-2026-murad-large-scale-multi-domain/"
---
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# MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset

**Authors:** [Serry Sibaee](https://md.research.elamur.ai/free/en/authors/serry-sibaee/), [Yasser Alhabashi](https://md.research.elamur.ai/free/en/authors/yasser-alhabashi/), Nadia Sibai, Yara Farouk, [Adel Ammar](https://md.research.elamur.ai/free/en/authors/adel-ammar/), Sawsan AlHalawani, [Wadii Boulila](https://md.research.elamur.ai/free/en/authors/wadii-boulila/) · **Year:** 2026 · **Venue:** arXiv:2601.21512 · **Licence:** [CC BY-NC-ND 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2601.21512) · [PDF](https://arxiv.org/pdf/2601.21512) · [DOI 10.48550/arxiv.2601.21512](https://doi.org/10.48550/arxiv.2601.21512) · [arXiv 2601.21512](https://arxiv.org/abs/2601.21512) · [OpenAlex](https://openalex.org/W7126262725)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Arabic has few large resources that pair words with formal definitions. This gap holds back reverse dictionaries, which let a user describe a concept and get back the matching term, a common fix for "tip of the tongue" moments. It also slows definition modeling and semantic retrieval. The authors built MURAD, an open set of 96,243 Arabic word–definition pairs drawn from 17 dictionaries and terminology glossaries. These range from classical works such as Al-Kafawi and Al-Jurjani to modern glossaries for physics, mathematics, psychology, engineering and AI. Printed books were digitized with Mistral OCR. Digital PDFs and HTML were parsed with scripts that use Unicode-range filters. GPT-4o then structured the raw text into term–definition entries, followed by normalization, cleaning and deduplication. Definitions were refined against eight lexicographic standards. The release includes the data, the full pipeline code and statistics that anyone can regenerate. The paper's contribution is the resource itself, which the authors present as the largest curated Arabic dictionary dataset. It offers a foundation for Arabic lexical semantics beyond the earlier KSAA-RD collection of about 58k words.

## Key points
- 96,243 records in UTF-8 CSV with three columns: Word, Definition, and Reference (a source ID). The data is on Hugging Face as `riotu-lab/MURAD`, and the code is in the GitHub repo `riotu-lab/RD-creation-library-RDCL`.
- There are 17 sources. The largest contributors are Al-Kafawi's Dictionary of Universals (14,476), the Encyclopedic Dictionary of Applied Linguistics Terms (13,350) and the Book of Terminology in Arabic Sciences (13,181). The smallest include the SDAIA Data and AI Glossary (1,243).
- Domains are assigned by source: General 38.41%, Scientific 29.15%, Islamic 18.57%, Linguistic 13.87%.
- Definitions are short. The median is 13 words and the mean is about 13.7–13.8, with a long right tail reaching 100 words.
- The authors compare MURAD with KSAA-RD/KSAA-CAD, SemEval-2022 CODWOE (which has no Arabic) and the Azhary ontology (which has relations but no definition text). The stated license is CC BY 4.0, yet the paper also describes the data as meant for non-commercial research.

## Interesting ideas
- The pipeline splits its work: deterministic scripts handle extraction and filtering, and an LLM handles only the hard part of turning messy text into structured pairs, which keeps most steps auditable. — For Mova (learn): Mova's LLM teacher tiers could follow the same pattern, letting the LLM structure noisy inputs while rule-based code does filtering and dedup, so each silver example has a traceable origin.
- Every entry carries a source tag, and the domain label is inherited from that tag rather than annotated by hand. This gives cheap, consistent metadata for every record. — For Mova (world): Compiled knowledge entries (verb classes, measures, ontology facts) could carry a provenance ID, so domain and trust levels follow from the source and explanations can cite where a fact came from.
- The reverse dictionary task maps a free description to the most precise standard term, which is the inverse of looking a word up. — For Mova (back): During generation from the meaning graph, a description-to-lemma lookup could choose the exact word for a cluster of semantic features instead of a vague paraphrase.
- Definitions from many dictionaries are rewritten against a fixed set of lexicographic standards, making glosses uniform enough to use as training targets. — For Mova (world): Normalizing WordNet and FrameNet glosses into a consistent template would make them easier to parse into UD trees and link to ontology nodes.
- All reported statistics are produced by released scripts run directly on the published data, with no hidden filtering, so others can reproduce every number. — For Mova (learn): Evaluation reports, including negative controls, could be generated entirely by versioned scripts over frozen data, so any regression claim can be re-run and checked.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sibaee2026murad,
  title = {{MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset}},
  author = {Sibaee, Serry and Alhabashi, Yasser and Sibai, Nadia and Farouk, Yara and Ammar, Adel and AlHalawani, Sawsan and Boulila, Wadii},
  year = {2026},
  doi = {10.48550/arxiv.2601.21512},
  eprint = {2601.21512},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2601.21512}
}
```

- [← previous Multimodal Reference by Means of the Pronoun We and Hand Gestures in a Novel Corpus of Parliamentary Opening Debates](https://md.research.elamur.ai/free/en/papers/navarretta-2026-multimodal-reference-means-pronoun-we/)
- [next → MUSIA: Multilingual Story Illustration Corpus for Cross-Cultural Alignment and Generation](https://md.research.elamur.ai/free/en/papers/tewari-2026-musia-multilingual-story-illustration-corpus/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/8/)

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