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Automated Amharic News Categorization Using Deep Learning Models.

Demeke Endalie1, Getamesay Haile1

  • 1Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Ethiopia.

Computational Intelligence and Neuroscience
|August 6, 2021
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This study introduces a deep learning model for Amharic news document classification, achieving 93.79% accuracy. The model leverages fastText and convolutional neural networks (CNNs) to overcome resource limitations in Amharic natural language processing.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Deep Learning

Background:

  • Limited language resources have hindered deep learning applications in Amharic document classification.
  • Traditional machine learning methods face challenges in effectively processing Amharic text semantics.

Discussion:

  • A novel deep learning model combining fastText for text vectorization and Convolutional Neural Networks (CNNs) was developed for Amharic news classification.
  • fastText generates semantic text vectors, addressing limitations of traditional methods.
  • The CNN's embedding layer automatically extracts relevant features from these vectors.

Key Insights:

  • The proposed deep learning model achieved a high classification accuracy of 93.79% on a dataset of six Amharic news categories.
  • Performance significantly outperformed established machine learning algorithms including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (DT), XGBoost (XGB), and Random Forest (RF).

Outlook:

  • This research paves the way for advanced natural language processing tasks in under-resourced languages like Amharic.
  • Future work could explore more sophisticated deep learning architectures and larger datasets to further enhance Amharic text understanding and classification.