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 mathematicallanguagemodel 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.
@article{arxiv.2104.05930,
title = {Distilling Wikipedia mathematical knowledge into neural network models},
author = {Joanne T. Kim and Mikel Landajuela and Brenden K. Petersen},
journal= {arXiv preprint arXiv:2104.05930},
year = {2022}
}