  Embedding words in high-dimensional vector spaces has proven valuable in many
natural language applications. In this work, we investigate whether
similarly-trained embeddings of integers can capture concepts that are useful
for mathematical applications. We probe the integer embeddings for mathematical
knowledge, apply them to a set of numerical reasoning tasks, and show that by
learning the representations from mathematical sequence data, we can
substantially improve over number embeddings learned from English text corpora.
