Advancing Natural Language Processing for Low-Resource Languages and Dialects

Dear Colleagues,

Natural Language Processing (NLP) has achieved remarkable success in high-resource languages; however, the majority of the world’s languages and dialects remain underrepresented due to limited data, linguistic diversity, and cultural complexity. In this Special Issue, we aim to address this imbalance by highlighting recent advances, challenges, and innovative solutions for low-resource languages and dialects, with a focus on methodologies that improve language understanding, generation, and classification when annotated resources are scarce or unavailable. Topics of interest include, but are not limited to, multilingual and crosslingual learning, transfer learning, zero-shot and few-shot approaches, dataset creation and annotation strategies, explainable AI, and culturally grounded NLP applications. Special attention is given to dialectal variations, code-mixing, sarcasm, and context-dependent meanings that are often overlooked in conventional models. By collating interdisciplinary research from linguistics, computer science, and social sciences, in this Special Issue we seek to foster inclusive NLP technologies that support digital equity and preserve linguistic diversity. The contributions are expected to advance both theoretical understanding and practical applications, enabling NLP systems to better serve underrepresented communities worldwide.

Dr. Tanjim Mahmud
Prof. Dr. Michal Ptaszynski
Prof. Dr. Karl Andersson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.