Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
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 ambiguiti
The increasing demand for robust, multilingual AI models and the standardization of lexical resources are driving the need for systematic digitization efforts like this.
Digitizing and standardizing linguistic data in non-English languages is crucial for developing inclusive and globally relevant AI systems, reducing data disparities, and fostering linguistic diversity in the AI landscape.
This initiative transforms a legacy print dictionary into a computational lexicon, making a valuable Arabic-English resource readily available for AI development and natural language processing applications.
- · Arabic NLP researchers
- · Multilingual AI developers
- · Digital lexicography
- · Arabic-speaking communities
- · Monolingual AI research paradigms
The Al-Mawrid Arabic-English dictionary becomes a standardized, machine-readable resource for AI model training and development.
Improved Arabic language understanding in AI systems could enhance global communication, information access, and cross-cultural AI applications.
The methodology could serve as a blueprint for digitizing other under-resourced languages, democratizing access to linguistic data for AI development globally.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL