Word Sequence Prediction Model for the Tigrigna Language Using a Deep Learning Approach

This research explores the development of a word sequence prediction model for the Tigrigna language using deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Tigrigna, primarily spoken in Eritrea and Ethiopia, faces significant challenges in natural language processing (NLP) due to the scarcity of comprehensive computational resources and annotated corpora. This study addresses the urgent need for effective NLP tools tailored to Tigrigna, focusing on the fundamental task of word sequence prediction, which underpins various applications such as machine translation and text generation. Despite the limited dataset of 10,000 sentences compiled from diverse sources, the models were evaluated for their ability to predict and generate coherent word sequences. Results indicate that while LSTM and GRU models demonstrated potential in capturing Tigrigna’s unique linguistic characteristics, they faced issues with overfitting and underfitting, particularly influenced by the choice of embeddings Word2Vec and Keras Embedding. The findings highlight the necessity for improved regularization techniques and the importance of data augmentation to enhance model generalization. This research contributes to the nascent field of Tigrigna NLP by demonstrating the applicability of deep learning models in resource-scarce languages. The outcomes suggest pathways for future advancements in Tigrigna language technology, emphasizing the potential for enhanced predictive text applications and deeper insights into Tigrigna's grammatical structures. Ultimately, this work lays a foundation for further developments in Tigrigna NLP, advocating for increased investment in linguistic resources and innovative modeling techniques to support the digital representation of the Tigrigna language.