© 2025 SSR Journal of Multidisciplinary (SSRJM) Published by SSR Publisher 124 SSR Journal of Multidisciplinary (SSRJM) Volume 2, Issue 3, 2025 Homepage: https://ssrpublisher.com/ssrjm/ ISSN: 3049-1304 Email: office.ssrpublisher@gmail.com A Hybrid Approach to Contextual Information Extraction in Low- Resource Igbo Uzoaru Godson Chetachi Department of Computer Science, Clifford University Owerrinta, Abia State, Nigeria Received: 19.05.2025 | Accepted: 24.06.2025 | Published: 07.07.2025 *Corresponding Author: Uzoaru Godson Chetachi DOI: 10.5281/zenodo.15832045 Abstract Original Research Article Citation: Uzoaru, G. C. (2025). A hybrid approach to contextual information extraction in low-resource Igbo. SSR Journal of Multidisciplinary (SSRJM), 2(3), 124-138. 1.0 INTRODUCTION In recent years, the demand for natural language processing (NLP) systems that can effectively understand and generate human language has significantly increased [ i ], particularly for under-resourced languages[ ii ] like Igbo[ iii ]. Traditional NLP techniques predominantly rely on the availability of large datasets for training[ iv ], which are often scarce or nonexistent in low-data environments [ v ]. Consequently, there is an urgent need for innovative methods that can leverage existing resources and enhance performance[ vi ]. One promising solution to this challenge is a hybrid approach that integrates various embedding techniques and transformer models to extract contextual information effectively [ vii ]. GloVe (Global Vectors for Word Representation) and FastText are two prominent word embedding models that offer unique advantages for low-data scenarios[ viii ]. GloVe captures global statistical information about word co- occurrences, providing dense vector representations that encapsulate semantic relationships[ ix ]. This model is particularly useful for capturing relationships between words based on their contexts in large corpora[ x ]. On the other hand, FastText enhances this representation by considering subword information[ xi ], which is especially beneficial for morphologically rich languages like Igbo, where prefixes and suffixes can alter meanings and functions significantly[ xii ]. This dual embedding strategy allows for a more nuanced understanding of the language, facilitating the extraction of contextual information even when training data is limited. Furthermore, the integration of Compact Convolutional Transformers (CCT) enhances the model's ability to process contextual relationships more efficiently[ xiii ]. CCTs aim to reduce the complexity associated with traditional transformer architectures[ xiv ] while maintaining effectiveness in capturing long-range dependencies and contextual nuances[ xv - xvi ]. This compact architecture is particularly advantageous in resource-constrained environments, allowing for faster training and inference times without sacrificing performance [ xvii ]. The combination of GloVe, FastText, and CCT not only addresses the data scarcity problem but also enhances the model's overall robustness in language processing tasks. Recent advances in the field have shown that hybrid models can outperform traditional methods in various NLP tasks[ xviii ]. For instance, [ xix ] demonstrated that combining multiple embeddings could significantly improve sentiment analysis accuracy in low-resource settings[ xx ]. Similarly, [ xxi ] found that leveraging subword information 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. Keywords: Low-resource languages, Natural Language Processing (NLP), Contextual information extraction, Compact Convolutional Transformer (CCT).