---
title: "Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary — paper by Fayed and Romary (2026)"
description: "Fayed and Romary propose a way to turn it into a machine-readable lexicon. 91% accuracy. 51 references."
authors: ["Diaa M. Fayed", "Laurent Romary"]
year: 2026
venue: "arXiv:2606.18205"
source: "https://arxiv.org/abs/2606.18205"
doi: "10.48550/arxiv.2606.18205"
arxiv: "2606.18205"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/fayed-2026-analyzing-encoding-al-mawrid-arabic/"
---
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# Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0

**Authors:** Diaa M. Fayed and [Laurent Romary](https://md.research.elamur.ai/free/en/authors/laurent-romary/) · **Year:** 2026 · **Venue:** [arXiv:2606.18205](https://md.research.elamur.ai/free/en/venues/language-resources-and-evaluation/) ([Language Resources and Evaluation 2026](https://md.research.elamur.ai/free/en/venues/language-resources-and-evaluation/2026/)) · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2606.18205) · [PDF](https://arxiv.org/pdf/2606.18205) · [DOI 10.48550/arxiv.2606.18205](https://doi.org/10.48550/arxiv.2606.18205) · [arXiv 2606.18205](https://arxiv.org/abs/2606.18205) · [OpenAlex](https://openalex.org/W7165036458)

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

## Summary
Al-Mawrid is one of the most widely used Arabic–English dictionaries, but it has never been converted into a standardized digital form. Fayed and Romary propose a way to turn it into a machine-readable lexicon. They combine two standards. ISO LMF (ISO 24,613) supplies the abstract lexical model, and TEI Lex-0 supplies the XML serialization. The work follows the "editorial view": the printed dictionary is treated as a meaningful linear sequence of tokens rather than a page layout. Typographic signals such as bold type, indentation, colons, commas and semicolons are interpreted as markers of headwords, senses, translation-equivalent groups, synonyms and cross-references. The authors analysed the dictionary's macro- and microstructure, wrote heuristic extraction rules, and tested the method on the letter Ayn. Structural parsing reached 91% accuracy, and extraction scores were high, especially recall for synonyms. The paper also explains where TEI Lex-0 struggles with Arabic phenomena. It describes an ID scheme intended for later export to Linked Open Data. The result is a reproducible workflow for retro-digitizing legacy bilingual Arabic dictionaries.

## Key points
- The source dictionary has roughly 35,000 headwords and about 60,000 subentries. The evaluation sample, the letter Ayn, covers 4.6% of the volume.
- Structural parsing accuracy was 91%. Synonym extraction reached 85% precision and 98% recall, and other morpho-semantic features reached 88% precision.
- About 77% of hyponymic links are implicit "open set" relations that the rule-based extraction cannot capture.
- Every lexical node receives a persistent identifier with a type prefix: me- for entries (me-000000 to me-999999), se- for senses, su- for subsenses. These are meant to become dereferenceable URIs after conversion to RDF with OntoLex-Lemon.
- Multiword expressions are classified as Transparent or Non-Transparent and encoded as `<relatedEntry>` nodes. Semicolon-separated translation groups become nested `<sense>` elements.

## Interesting ideas
- The method treats punctuation and layout in a reference work as a structural grammar, mapping each delimiter to a specific relation such as a sense boundary or a synonym list. This turns semi-structured text into typed records without needing any ML.
- Each node type gets its own ID prefix (entry, sense, subsense), so the type of any reference is visible from the ID alone and the whole resource becomes a predictable, queryable network.
- Multiword expressions are split by semantic transparency, and the opaque ones are stored as separate lexical units instead of being composed from their parts.
- The evaluation reports precision and recall separately for each relation type, and it explicitly measures how many relations lie beyond what rules can reach.
- The manual heuristic annotation is framed as a future gold standard for training classifiers that will automate the same decisions.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): A similar delimiter grammar could parse WordNet glosses or Wiktionary definitions into explicit sense and synonym links for the compiled ontology, and each link would stay traceable to its source punctuation.
- **I2** (world): Typed prefixes for knowledge-graph nodes (verb class, frame, sense, entity) would make explanation traces readable and allow cheap sanity checks when a node of the wrong type is referenced.
- **I3** (tokens): The lemma lexicon could mark non-transparent idioms as single units carrying their own meaning, while transparent combinations are left to normal compositional analysis.
- **I4** (learn): Each repair rule or extractor could log the share of cases it cannot cover, and that remainder could be routed to the LLM teacher tier instead of being silently counted as misses.
- **I5** (learn): Hand-checked decisions from the explainable rule pipeline could be stored deliberately as training and evaluation data for later learned components, so that rules seed models rather than compete with them.

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

## Institutions

- [Institut national de recherche en sciences et technologies du numérique](https://md.research.elamur.ai/free/en/labs/institut-national-de-recherche-en-sciences-et-technologies-du-numerique/) — Research institute, Le Chesnay-Rocquencourt, France · 62 works · 17,271 citations

Also on the byline: [Sinai University](https://ror.org/01dd13a92).

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## Cite

Cite as: Diaa M. Fayed and Laurent Romary. 2026. Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0. arXiv:2606.18205.

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

```bibtex
@misc{fayed2026analyzing,
  title = {{Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0}},
  author = {Diaa M. Fayed and Laurent Romary},
  year = {2026},
  doi = {10.48550/arxiv.2606.18205},
  eprint = {2606.18205},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2606.18205}
}
```

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## References

51 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*