An Amharic News Text classification Dataset
This dataset addresses a data scarcity problem for NLP researchers and practitioners working with Amharic, enabling implementation of existing models, though it is incremental as it focuses on data creation rather than novel methods.
The authors tackled the lack of labeled training data for text classification in low-resource languages by introducing an Amharic news text classification dataset with over 50k articles categorized into 6 classes, providing baseline performances to facilitate research.
In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.