Automatic Amharic text news classification: Aneural networks approach

W Kelemework

W Kelemework School of Computing and Electrical Engineering, Institute of Technology, Bahir Dar University

Keywords: Learning Vector Quantization (LVQ), Text news classification, Term Frequency (TF), Term Frequency by Inverse Document Frequency (TF*IDF)

Abstract

The study is on classification of Amharic news automatically using neural networks approach. Learning Vector Quantization (LVQ) algorithm is employed to classify new instance of Amharic news based on classifier developed using training dataset. Two weighting schemes, Term Frequency (TF) and Term Frequency by Inverse Document Frequency (TF*IDF), are used to weight the features or keywords in news documents. Based on the two weighting methods, news by features matrix is generated and fed to LVQ. Using the TF weighting method, 94.81%, 61.61% and 70.08% accuracies are obtained at three, six and nine classes experiments respectively with an average of 75.5% accuracy. For similar experiments, the application of TF*IDF weighting method resulted in 69.63%, 78.22% and 68.03% accuracies with an average of 71.96% accuracy.

Author Biography

W Kelemework, School of Computing and Electrical Engineering, Institute of Technology, Bahir Dar University

The study is on classification of Amharic news automatically using neural networks approach. Learning Vector Quantization (LVQ) algorithm is employed to classify new instance of Amharic news based on classifier developed using training dataset. Two weighting schemes, Term Frequency (TF) and Term Frequency by Inverse Document Frequency (TF*IDF), are used to weight the features or keywords in news documents. Based on the two weighting methods, news by features matrix is generated and fed to LVQ. Using the TF weighting method, 94.81%, 61.61% and 70.08% accuracies are obtained at three, six and nine classes experiments respectively with an average of 75.5% accuracy. For similar experiments, the application of TF*IDF weighting method resulted in 69.63%, 78.22% and 68.03% accuracies with an average of 71.96% accuracy.