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PUBLICATIONS

Extracting Mathematical Concepts from Text

Published

Author(s)

Jacob Collard, Valeria de Paiva, Brendan Fong, Eswaran Subrahmanian

Abstract

We investigate some different systems for extracting mathematical entities from texts in the mathematical field of category theory, as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare their results. This small experiment showcases some of the issues with the construction and evaluation of specific domain knowledge graphs. We also make available two open corpora in research mathematics, in particular in category theory: a small corpus of 755 abstracts from the journal TAC (3188 sentences), and a bigger corpus from the nLab community wiki (15,000 sentences).
Proceedings Title
Proceedings of the 29th International Conference on Computational Linguistics
Conference Dates
October 12-17, 2022
Conference Location
Gyeongju, KR
Conference Title
29th International Conference on Computational Linguistics
Pub Type
Conferences

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Keywords

category theory, natural language processing, evaluation, mathematics, technical language processing, key phrase extraction
Natural language processing, Mathematics and statistics, Information retrieval and Computational science

Citation

Collard, J. , de Paiva, V. , Fong, B. and Subrahmanian, E. (2022), Extracting Mathematical Concepts from Text, Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, KR, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934895 (Accessed October 9, 2025)

Issues

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Created October 12, 2022, Updated May 10, 2023
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