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