Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data. In contrast, the field of natural language processing leverages resources like Wikipedia that provide enormous amounts of real-world textual data. Adopting the philosophy of mathematics as language, we bridge this gap by introducing a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks. We demonstrate that a $textit{mathematical}$ $textit{language}$ $textit{model}$ trained on this corpus of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression.
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