Advancing Natural Language Processing for Low-Resource Languages and Dialects
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).
Deadline for manuscript submissions: 31 July 2026 | Viewed by 73
Special Issue Editors
Interests: AI; evolutionary computing and image processing; NLP; AI in healthcare and agriculture
Interests: abusive text detection; affect and sentiment analysis; affective computing (AC); Ainu language; artificial intelligence (AI); automatic cyberbullying detection; computational linguistics (CLs); corpus linguistics; emotional intelligence; human–computer interaction (HCI); large language models; linguistics; natural language processing (NLP); offensive text detection; philosophy of emotions; pragmatics
Special Issues, Collections and Topics in MDPI journals
Interests: pervasive and mobile computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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Keywords
- low-resource and dialect natural language processing
- multilingual and crosslingual NLP
- language resources and dataset creation
- dialect and code-mixed language processing
- NLP for underrepresented languages
- machine learning and deep learning for NLP
- linguistic and cultural adaptation in NLP
- ethical and inclusive AI for language technologies
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