Pegi1727/Decolonizing-the-Algorithm-DAR: DAR Framework: Dynamic Adaptive Representation for Low-Resource Languages

This release introduces the DAR (Dynamic Adaptive Representation) framework, a novel approach to vector space modeling in Computational Linguistics, specifically optimized for languages with limited digital footprints. ‌ Key Innovations: Adaptive Vectorization: Unlike static embeddings, DAR adjusts representation weights based on contextual and morphological density. Cross-Lingual Mapping: Efficiently maps low-resource linguistic structures onto high-resource semantic spaces without losing unique cultural-linguistic nuances. Resilience to Data Sparsity: Engineered to maintain high performance in NLP tasks (like NER or Sentiment Analysis) even with minimal training sets. ‌ Integration: DAR is designed to work in synergy with the DIINA architecture, providing the flexible data structures necessary for dynamic inhibitory processing. This dual-framework approach addresses the "Digital Justice" gap by empowering underrepresented languages in the AI landscape. ‌ Lead Architect: Dr. Pegah Merrikhi Focus: NLP / Linguistic Justice / Computational Modeling