219152853/Oshiwambo-NLP-Hybrid-Reconstruction

# Oshiwambo-NLP-Hybrid-Reconstruction This repository contains the implementation of a Hybrid AI framework designed to preserve and revive the Oshiwambo language through advanced Natural Language Processing (NLP). This work was developed as part of a Master of Data Science thesis at the Namibia University of Science and Technology (NUST). ## 🚀 Project Overview The system addresses the dialectal variations within Oshiwambo by utilizing a dual-pathway architecture that combines deterministic dataset matching with predictive deep learning models. ### Key Objectives: 1. **Morphological Peeling:** An N-gram based mechanism to isolate semantic roots from complex Oshiwambo word structures. 2. **Hybrid Feature Fusion:** A deep learning engine combining **CNNs** (for spatial morphological fingerprinting) and **Bidirectional LSTMs** (for sequential context analysis), resulting in a 768-dimensional feature vector. 3. **Agglutinative Synthesis:** A generative module that reconstructs correct linguistic forms for "Out-of-Vocabulary" terms using root preservation and affix extraction. ## 🛠️ Tech Stack - **Languages:** Python - **Frameworks:** TensorFlow, Keras, Scikit-learn - **Libraries:** Pandas, NumPy, RapidFuzz (for fuzzy string matching) - **Deployment:** Streamlit (Web UI) ## 🏗️ Architecture The system utilizes a dual-pathway logic: - **Deterministic Path:** Uses SVM and Min-Max scaling for high-accuracy classification of known vocabulary. - **Predictive Path:** Employs the CNN + Bi-LSTM fusion layer for unknown or evolving dialectal tokens. ## 📁 Repository Structure - `/data`: Empirical datasets and dialectal feature indices. - `/models`: Saved weights for the CNN-LSTM hybrid architecture. - `/src`: Core logic for morphological peeling and agglutinative synthesis. - `app.py`: The Streamlit interface for live testing and prediction. **Author:** Raban S. Raban **Supervision:** Prof. Hippolyte N’sung-Nza Muyingi *Department of Informatics, NUST.*