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

2026-06-16

AI Summary

This study addresses the lack of digitized Arabic bilingual dictionaries and their structural ambiguity by proposing an innovative framework that integrates ISO Lexical Markup Framework (LMF) and TEI Lex-0 standards to systematically reverse-engineer the *Al-Mawrid* Arabic–English dictionary. The framework explicitly models implicit semantic relations unique to Arabic and incorporates a prefix-based referencing mechanism designed for seamless integration into the Linguistic Linked Open Data (LLOD) cloud. Evaluated on a sample of entries under the Arabic letter Ayn, the approach achieves 91% accuracy in structural parsing, 85% precision and 98% recall in synonym extraction, and 88% precision for other morphosemantic features. The resulting resource constitutes a high-fidelity, reproducible, machine-readable, and semantically interoperable lexical infrastructure for Arabic.

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.

Problem

• Arabic lexical infrastructure
• digitization
• bilingual dictionary
• lexical encoding
• legacy lexicon

Innovation

• Lexical Markup Framework (LMF)
• TEI Lex-0
• Arabic computational lexicography
• Linguistic Linked Open Data (LLOD)
• legacy dictionary digitization