jumezurike/LrLM

Low Resource Language Model using neural network base Deep learning Technique # LrLM Low Resource Language Model using neural network based Deep learning Technique This study explores the unique morphological structure of the Igbo, Yoruba and other language of the same family, focusing on the invariant nature of root words and their semantic enhancement through affixation. In Igbo, root words remain constant while their meanings are modulated by prefixes and suffixes, forming meaningful combinations that adhere to vowel-consonant-vowel patterns. Examples such as "Ngugu" (lungs) and phrases like "Gu-pu ya" (remove it) and "Go-zie" (bless) illustrate how roots and morphemes interact to convey different meanings. Traditional language models, often based on subject-verb-object structures typical of English, fall short in accurately representing Igbo. Therefore, we propose a deep learning strategy using neural networks where vowels act as activators to capture these morphological patterns. This model leverages a shift-left approach and security-by-design principles to ensure robust performance and secure data handling. Incorporating keyless encryption methods, which are resilient against quantum computing threats, guarantees the security and privacy of data, transactions, and communications within the model. The algorithm, reinforced by human feedback, forms a dynamic matrix that can represent any conceivable word or phrase in Igbo, significantly enhancing AI tasks such as translation, speech recognition, and text generation. By leveraging this approach, we aim to bridge the gap in AI representation for low-resource languages, offering a pathway to more inclusive and culturally relevant AI applications. This work invites collaboration to harness Igbo’s linguistic richness for advanced AI development while ensuring security and privacy in the post-quantum era. ### The three main deliverables: 1. Build a Low resource foundational language model using neural network deep learning techniques 2. Train the models using ML (supervise, unsupervised, …