Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0

arXiv cs.CL Papers
arabic-nlp computational-lexicon text-encoding digital-humanities bilingual-dictionary lexical-resource language-markup

Summary

This paper presents a methodology for digitizing the Al-Mawrid Arabic-English dictionary using ISO LMF and TEI Lex-0 standards, achieving high parsing accuracy and precision, and addressing gaps in Arabic lexical infrastructure.

arXiv:2606.18205v1 Announce Type: new Abstract: This paper presents a robust methodology for the systematic digitization and encoding of the Al-Mawrid Arabic-English dictionary, transforming it from a legacy print resource into a standardized computational lexicon. Addressing a significant gap in Arabic lexical infrastructure, the study adopts a dual-standard framing that aligns the ISO Lexical Markup Framework (LMF) with the Text Encoding Initiative TEI Lex-0 guidelines. By applying an editorial view to the dictionary's macro- and microstructure, the research resolves the structural ambiguities and punctuation inconsistencies typical of 20th-century bilingual dictionaries. The methodology is grounded in an empirical analysis of the dictionary's lexical knowledge density. Drawing on a representative sample (the letter Ayn, comprising 4.6% of the total volume), the study provides scientific weight to the encoding process, demonstrating a structural parsing accuracy of 91%. Quantitative evaluation of the information extraction rules reveals high performance, with 85% precision and 98% recall for synonyms, and 88% precision for other morpho-semantic features. Beyond technical description, the paper provides a critical comparison with existing Arabic lexical resources and discusses the limitations of TEI Lex-0 when modelling specific Arabic phenomena, such as implicit "open set" semantic relations and scattered morphological cues. Furthermore, the study explores the potential for Linguistic Linked Open Data (LLOD) integration by establishing a scalable prefix-based referencing system that facilitates the resource's inclusion in the semantic web. The result is an interoperable, machine-tractable resource that provides a reproducible workflow for the retro-digitization of complex legacy bilingual lexicons within the Arabic NLP and Digital Humanities communities.
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# Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
Source: [https://arxiv.org/abs/2606.18205](https://arxiv.org/abs/2606.18205)
[View PDF](https://arxiv.org/pdf/2606.18205)

> Abstract:This paper presents a robust methodology for the systematic digitization and encoding of the Al\-Mawrid Arabic\-English dictionary, transforming it from a legacy print resource into a standardized computational lexicon\. Addressing a significant gap in Arabic lexical infrastructure, the study adopts a dual\-standard framing that aligns the ISO Lexical Markup Framework \(LMF\) with the Text Encoding Initiative TEI Lex\-0 guidelines\. By applying an editorial view to the dictionary's macro\- and microstructure, the research resolves the structural ambiguities and punctuation inconsistencies typical of 20th\-century bilingual dictionaries\. The methodology is grounded in an empirical analysis of the dictionary's lexical knowledge density\. Drawing on a representative sample \(the letter Ayn, comprising 4\.6% of the total volume\), the study provides scientific weight to the encoding process, demonstrating a structural parsing accuracy of 91%\. Quantitative evaluation of the information extraction rules reveals high performance, with 85% precision and 98% recall for synonyms, and 88% precision for other morpho\-semantic features\. Beyond technical description, the paper provides a critical comparison with existing Arabic lexical resources and discusses the limitations of TEI Lex\-0 when modelling specific Arabic phenomena, such as implicit "open set" semantic relations and scattered morphological cues\. Furthermore, the study explores the potential for Linguistic Linked Open Data \(LLOD\) integration by establishing a scalable prefix\-based referencing system that facilitates the resource's inclusion in the semantic web\. The result is an interoperable, machine\-tractable resource that provides a reproducible workflow for the retro\-digitization of complex legacy bilingual lexicons within the Arabic NLP and Digital Humanities communities\.

## Submission history

From: Diaa Fayed \[[view email](https://arxiv.org/show-email/e2f61111/2606.18205)\] **\[v1\]**Tue, 16 Jun 2026 17:35:11 UTC \(1,089 KB\)

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