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Natural language provides an intuitive and expressive way of conveying human intent to robots. Prior works employed end-to-end methods for learning trajectory deformations from language corrections. However, such methods do not generalize to new initial trajectories or object configurations. This work presents ExTraCT, a modular framework for trajectory corrections using natural language that combines Large Language Models (LLMs) for natural language understanding and trajectory deformation functions. Given a scene, ExTraCT generates the trajectory modification features (scene-specific and scene-independent) and their corresponding natural language textual descriptions for the objects in the scene online based on a template. We use LLMs for semantic matching of user utterances to the textual descriptions of features. Based on the feature matched, a trajectory modification function is applied to the initial trajectory, allowing generalization to unseen trajectories and object configurations. Through user studies conducted both in simulation and with a physical robot arm, we demonstrate that trajectories deformed using our method were more accurate and were preferred in about 80\% of cases, outperforming the baseline. We also showcase the versatility of our system in a manipulation task and an assistive feeding task., Comment: 11 pages, 7 figures