L-ReLF: A Framework for Lexical Dataset Creation
This addresses the lack of standardized terminology for low-resource languages, enabling better knowledge equity and downstream NLP applications, though it is incremental as it builds on existing methods like OCR and Wikidata.
The paper tackles the problem of creating high-quality lexical datasets for underserved languages like Moroccan Darija, resulting in a reproducible framework that produces structured datasets compatible with Wikidata Lexemes.
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.