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arXiv · 2603.29346

L-ReLF: A Framework for Lexical Dataset Creation

Anass Sedrati·Mounir Afifi·Reda Benkhadra
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Abstract

This paper introduces the L-ReLF (Low-Resource Lexical Framework), a novel, reproducible methodology for creating high-quality, structured lexical datasets for underserved languages. The lack of standardized terminology, exemplified by Moroccan Darija, poses a critical barrier to knowledge equity in platforms like Wikipedia, often forcing editors to rely on inconsistent, ad-hoc methods to create new words in their language. Our research details the technical pipeline developed to overcome these challenges. We systematically address the difficulties of working with low-resource data, including source identification, utilizing Optical Character Recognition (OCR) despite its bias towards Modern Standard Arabic, and rigorous post-processing to correct errors and standardize the data model. The resulting structured dataset is fully compatible with Wikidata Lexemes, serving as a vital technical resource. The L-ReLF methodology is designed for generalizability, offering other language communities a clear path to build foundational lexical data for downstream NLP applications, such as Machine Translation and morphological analysis.

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Anass Sedrati, Mounir Afifi, Reda Benkhadra. 2026-03-31. L-ReLF: A Framework for Lexical Dataset Creation. https://doi.org/10.1109/icnlp69856.2026.11528140

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