L-ReLF: A Framework for Lexical Dataset Creation

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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.

Comments: Accepted to the 2026 International Conference on Natural Language Processing (ICNLP). 6 pages, 1 figure

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2603.29346 [cs.CL]

(or arXiv:2603.29346v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.29346

arXiv-issued DOI via DataCite (pending registration)

Journal reference: Proceedings of the 2026 International Conference on Natural Language Processing (ICNLP)

Submission history

From: Anass Sedrati [view email] [v1] Tue, 31 Mar 2026 07:19:00 UTC (330 KB)