Automatic Amharic Part of Speech Tagging (AAPOST): A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods

Abstract

Part of speech (POS) tagging is an initial task for many natural language applications. POS tagging for Amharic is in its infancy. This study contributes towards the improvement of Amharic POS tagging by experimenting using Deep Learning and Conditional Random Fields (CRF) approaches. Word embedding is integrated into the system to enhance performance. The model was applied to an Amharic news corpus tagged into 11 major part of speeches and achieved accuracies of 91.12% and 90% for the Bidirectional LSTM and CRF methods respectively. The result shows that the Bidirectional LSTM approach performance is better than the CRF method. More enhancement is expected in the future by increasing the size and diversity of Amharic corpus.