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Nowadays, Machine Learning (ML) is seen as the universal solution to improvethe effectiveness of information retrieval (IR) methods. However, whilemathematics is a precise and accurate science, it is usually expressed by lessaccurate and imprecise descriptions, contributing to the relative dearth ofmachine learning applications for IR in this domain. Generally, mathematicaldocuments communicate their knowledge with an ambiguous, context-dependent, andnon-formal language. Given recent advances in ML, it seems canonical to applyML techniques to represent and retrieve mathematics semantically. In this work,we apply popular text embedding techniques to the arXiv collection of STEMdocuments and explore how these are unable to properly understand mathematicsfrom that corpus. In addition, we also investigate the missing aspects thatwould allow mathematics to be learned by computers.

Why Machines Cannot Learn Mathematics, Yet