  In this paper, we fine-tuned three pre-trained BERT models on the task of
"definition extraction" from mathematical English written in LaTeX. This is
presented as a binary classification problem, where either a sentence contains
a definition of a mathematical term or it does not. We used two original data
sets, "Chicago" and "TAC," to fine-tune and test these models. We also tested
on WFMALL, a dataset presented by Vanetik and Litvak in 2021 and compared the
performance of our models to theirs. We found that a high-performance
Sentence-BERT transformer model performed best based on overall accuracy,
recall, and precision metrics, achieving comparable results to the earlier
models with less computational effort.
