  We present an unsupervised approach for discovering semantic representations
of mathematical equations. Equations are challenging to analyze because each is
unique, or nearly unique. Our method, which we call equation embeddings, finds
good representations of equations by using the representations of their
surrounding words. We used equation embeddings to analyze four collections of
scientific articles from the arXiv, covering four computer science domains
(NLP, IR, AI, and ML) and $\sim$98.5k equations. Quantitatively, we found that
equation embeddings provide better models when compared to existing word
embedding approaches. Qualitatively, we found that equation embeddings provide
coherent semantic representations of equations and can capture semantic
similarity to other equations and to words.
