# Equation Embeddings

arXiv: https://arxiv.org/abs/1803.09123 · HTML: https://mlsanity.com/paper/1803.09123/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Semantic Representations of Mathematical Expressions in a Continuous Vector Space](https://mlsanity.com/paper/2211.08142/) (2022)
- [E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions](https://mlsanity.com/paper/2501.14951/) (2025)
- [Changes from Classical Statistics to Modern Statistics and Data Science](https://mlsanity.com/paper/2211.03756/) (2022)
- [A semantic-based deep learning approach for mathematical expression retrieval](https://mlsanity.com/paper/2511.01364/) (2025)
- [Syntax Meets Semantics: Understanding Scientific Formulae](https://mlsanity.com/paper/2608.02457/) (2026)

## Abstract (arXiv)

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 ~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.