| Title: |
AI Low-Resource Language Models for African Indigenous Languages: Bridging the Digital Divide Through Innovative AI Natural Language Processing Solutions. |
| Authors: |
Moolu Venture Lab |
| Contributors: |
Ogbonna, Prince; Ikechukwu, Micheal |
| Publisher Information: |
Zenodo |
| Publication Year: |
2024 |
| Collection: |
Zenodo |
| Description: |
This research investigates the development and optimization of natural language processing (NLP) models for African indigenous languages, addressing the critical digital divide that affects over 2 billion speakers across the continent. Through a comprehensive analysis of existing pre-trained language models and novel methodologies, this study examines the effectiveness of transformer-based architectures, specifically focusing on multilingual BERT variants, sentiment analysis systems, and speech recognition models for low-resource African languages. The research employs a design thinking framework to develop scalable solutions that enhance digital inclusion and educational accessibility. Our findings demonstrate that ensemble methods combining multiple pre-trained language models achieve superior performance, with weighted F1 scores exceeding 77% for closely related language families. The study contributes to the growing body of work in Afrocentric NLP by providing empirical evidence for effective cross-linguistic transfer learning techniques and proposing a framework for sustainable language model development in resource-constrained environments |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
https://zenodo.org/records/16786902; oai:zenodo.org:16786902; https://doi.org/10.5281/zenodo.16786902 |
| DOI: |
10.5281/zenodo.16786902 |
| Availability: |
https://doi.org/10.5281/zenodo.16786902; https://zenodo.org/records/16786902 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.DFCDC468 |
| Database: |
BASE |