# L-ReLF: A Framework for Lexical Dataset Creation

arXiv: https://arxiv.org/abs/2603.29346 · HTML: https://mlsanity.com/paper/2603.29346/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect](https://mlsanity.com/paper/2409.17912/) (2024)
- [Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil](https://mlsanity.com/paper/2507.18264/) (2025)
- [Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Study](https://mlsanity.com/paper/2405.16669/) (2024)
- [The Evolution of Darija Open Dataset: Introducing Version 2](https://mlsanity.com/paper/2405.13016/) (2024)
- [Sentiment Analysis Dataset in Moroccan Dialect: Bridging the Gap Between Arabic and Latin Scripted dialect](https://mlsanity.com/paper/2303.15987/) (2023)

## Abstract (arXiv)

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.