Deep Learning-Based Part-of-Speech Tagging of the Tigrinya Language

Abstract

Deep Neural Networks have demonstrated the great efficiency in many NLP task for various languages. Unfortunately, some resource-scarce languages as, e.g., Tigrinya still receive too little attention, therefore many NLP applications as part-of-speech tagging are in their early stages. Consequently, the main objective of this research is to offer the effective part-of-speech tagging solutions for the Tigrinya language having rather small training corpus. In this paper the Deep Neural Network classifiers (i.e., Feed Forward Neural Network, Long Short-Term Memory, Bidirectional LSTM and Convolutional Neural Network) are investigated by applying them on a top of trained distributional neural word2vec embeddings. Seeking for the most accurate solutions, DNN models are optimized manually and automatically. Despite automatic hyper-parameter optimization demonstrates a good performance with the Convolutional Neural Network, the manually tested Bidirectional Long Short – Term Memory method achieves the highest overall accuracy equal to 0.91%.