This paper presents WikiMorph, a tool that automatically breaks down words into morphemes, etymological compounds (morphemes from root languages), and generates contextual definitions for each component. It comes in two flavors: a dataset and a deep-learning-based model. The dataset was extracted from Wiktionary and contains over 450k entries. We then used this dataset to train a GPT-2 model to generalize and decompose any word into morphemes and their definitions. We find that the model accurately generates complex breakdowns when given a high-quality initial definition. [This paper was published in: "Proceedings of the 22nd International Conference on Artificial Intelligence in Education," edited by I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, and V. Dimitrova, Springer International Publishing, 2021, pp. 406-11.]