# TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

arXiv: https://arxiv.org/abs/1902.06034 · HTML: https://mlsanity.com/paper/1902.06034/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2019.

## Built on by (most cited first, 10 of 10)

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- [Hierarchical Catalogue Generation for Literature Review: A Benchmark](https://mlsanity.com/paper/2304.03512/) (2023)
- [cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers](https://mlsanity.com/paper/2406.08398/) (2024)
- [A Survey on Semantics in Automated Data Science](https://mlsanity.com/paper/2205.08018/) (2022)
- [A Vision for Semantically Enriched Data Science](https://mlsanity.com/paper/2303.01378/) (2023)
- [SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity](https://mlsanity.com/paper/2606.16003/) (2026)
- [Syntax Meets Semantics: Understanding Scientific Formulae](https://mlsanity.com/paper/2608.02457/) (2026)

## Abstract (arXiv)

Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware equation generation, equation topic inference, and topic-aware alignment of mathematical symbols and words.