Enhancing NLP for low-resource languages (LRLs) is vital for supporting linguistic diversity, yet a fundamental challenge lies in the cross-lingual semantic gap. To address this, a common approach is to initialize LRL word embeddings with those from high-resource languages, thereby enriching their semantic space. However, subsequent embedding adaptation is typically performed through uniform updates across the vocabulary, which fails to allocate larger corrections to semantically difficult or infrequent tokens (e.g., culturally specific terms in Amharic or Somali) that need them most. To address this, we propose Selective Embedding Correction (SEC), a parameter-efficient framework for dynamic semantic correction. SEC models embedding adjustments as sparse low-rank residuals regularized by a group-lasso objective. This enables data-driven selectivity that refines poorly aligned tokens while preserving well-aligned representations. Experiments on named entity recognition, part-of-speech tagging, and natural language inference across multiple low-resource languages show that SEC achieves strong improvements over baselines with only about 1% additional trainable parameters.