A Hybrid Approach to Contextual Information Extraction in Low-Resource Igbo

Extracting contextual information from low-resource languages such as Igbo remains a significant challenge due to limited linguistic data. This paper proposes a novel hybrid approach that leverages both global and subword-level information to address this limitation. A hybrid embedding framework, combining GloVe and FastText embeddings, is employed to capture rich semantic and syntactic information. These embeddings are then integrated into a Compact Convolutional Transformer (CCT) architecture, which replaces the computationally intensive self-attention mechanism with efficient convolutional layers. This design enables effective capture of local and global dependencies while reducing computational costs. Experimental results on small, domain-specific Igbo datasets, including customer support and medical dialogues, demonstrate the superior performance of the proposed model over baseline approaches. The hybrid model achieves higher accuracy and F1 scores, highlighting its potential to improve NLP performance in low-resource settings. This work contributes to the advancement of natural language processing for underrepresented languages.