Abstract:
For decades, machine learning techniques have been used to process Amharic texts. (e potential application of deep learning on
Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep
learning model for Amharic news document classification. (e proposed model uses fastText to generate text vectors to represent
semantic meaning of texts and solve the problem of traditional methods. (e text vectors matrix is then fed into the embedding
layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with
six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known
machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost
(XGB), and random forest (RF) and achieved good results
