L-ReLF: A Framework for Lexical Dataset Creation

• URL: http://arxiv.org/abs/2603.29346v1
• Date: Tue, 31 Mar 2026 07:19:00 GMT
• Title: L-ReLF: A Framework for Lexical Dataset Creation
• Abstract summary: The lack of standardized terminology poses a critical barrier to knowledge equity in platforms like Wikipedia.<n>The L-ReLF methodology is designed for generalizability, offering other language communities a clear path to build lexical data for downstream NLP applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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