Abstract
Automating discovery in mathematics and science will require sophisticated methods of information extraction and abstract reasoning, including models that can convincingly process relationships between mathematical elements and natural language, to produce problem solutions of real-world value. We analyze mathematical language processing methods across five strategic sub-areas (identifier-definition extraction, formula retrieval, natural language premise selection, math word problem solving, and informal theorem proving) from recent years, highlighting prevailing methodologies, existing limitations, overarching trends, and promising avenues for future research.- Anthology ID:
- 2023.tacl-1.66
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 11
- Month:
- Year:
- 2023
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1162–1184
- Language:
- URL:
- https://aclanthology.org/2023.tacl-1.66
- DOI:
- 10.1162/tacl_a_00594
- Cite (ACL):
- Jordan Meadows and André Freitas. 2023. Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks. Transactions of the Association for Computational Linguistics, 11:1162–1184.
- Cite (Informal):
- Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks (Meadows & Freitas, TACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.tacl-1.66.pdf