# Parmesan: mathematical concept extraction for education

arXiv: https://arxiv.org/abs/2307.06699 · HTML: https://mlsanity.com/paper/2307.06699/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency](https://mlsanity.com/paper/1911.01678/) (2019)
- [WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context](https://mlsanity.com/paper/2004.15016/) (2020)
- [Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks](https://mlsanity.com/paper/2011.04521/) (2020)
- [Extracting Mathematical Concepts from Text](https://mlsanity.com/paper/2208.13830/) (2022)
- [Fast Linking of Mathematical Wikidata Entities in Wikipedia Articles Using Annotation Recommendation](https://mlsanity.com/paper/2104.05111/) (2021)
- [An Algebraic Framework for Structured Epidemic Modeling](https://mlsanity.com/paper/2203.16345/) (2022)

## Built on by (most cited first, 2 of 2)

- [Fine-Tuning BERTs for Definition Extraction from Mathematical Text](https://mlsanity.com/paper/2406.13827/) (2024)
- [Extracting Mathematical Concepts with Large Language Models](https://mlsanity.com/paper/2309.00642/) (2023)

## Abstract (arXiv)

Mathematics is a highly specialized domain with its own unique set of challenges that has seen limited study in natural language processing. However, mathematics is used in a wide variety of fields and multidisciplinary research in many different domains often relies on an understanding of mathematical concepts. To aid researchers coming from other fields, we develop a prototype system for searching for and defining mathematical concepts in context, focusing on the field of category theory. This system, Parmesan, depends on natural language processing components including concept extraction, relation extraction, definition extraction, and entity linking. In developing this system, we show that existing techniques cannot be applied directly to the category theory domain, and suggest hybrid techniques that do perform well, though we expect the system to evolve over time. We also provide two cleaned mathematical corpora that power the prototype system, which are based on journal articles and wiki pages, respectively. The corpora have been annotated with dependency trees, lemmas, and part-of-speech tags.