Abstract
This paper proposes a scheme for classifying news articles in Tigrinya, a language spoken in northern Ethiopia and Eritrea, and is known for its lack of extensive and readily available data. We present the first publicly available news article dataset for Tigrinya, containing 2396 articles. In addition, we propose a data augmentation method for text classification. Furthermore, we explore the performance in text classification using traditional machine learning methods (support vector machine, logistic regression, random forest, linear discriminant analysis, decision tree, and Naive Bayes), a neural network-based model (bidirectional long short term memory), and a transformer-based model (TigRoBERTa). The experimental results show that the proposed method performs better in accuracy than the comparative methods up to nine points. The codes and the dataset are open to the research community (https://github.com/mehari-eng/Article-News-Categorization).
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Yohannes, H.M., Amagasa, T. (2022). A Scheme for News Article Classification in a Low-Resource Language. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_47
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