Extracting Definienda in Mathematical Scholarly Articles with Transformers

Shufan Jiang; Pierre Senellart

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arXiv · 2311.12448

Extracting Definienda in Mathematical Scholarly Articles with Transformers

Shufan Jiang·Pierre Senellart

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Abstract

We consider automatically identifying the defined term within a mathematical definition from the text of an academic article. Inspired by the development of transformer-based natural language processing applications, we pose the problem as (a) a token-level classification task using fine-tuned pre-trained transformers; and (b) a question-answering task using a generalist large language model (GPT). We also propose a rule-based approach to build a labeled dataset from the LATEX source of papers. Experimental results show that it is possible to reach high levels of precision and recall using either recent (and expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.

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Shufan Jiang, Pierre Senellart. 2023-11-21. Extracting Definienda in Mathematical Scholarly Articles with Transformers. https://arxiv.org/abs/2311.12448

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