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.

本研究使用三种预训练的 BERT 模型对数学英语 (使用 LaTeX 编写) 的 “定义提取” 任务进行微调，将其作为一个二元分类问题。通过对原始数据集 “芝加哥” 和 “TAC” 的微调和测试，以及与 Vanetik 和 Litvak 在 2021 年提出的 WFMALL 数据集的对比测试，我们发现高性能的 Sentence-BERT transformer 模型在整体准确度、召回率和精确度等指标上表现最佳，且相较于早期模型，所需的计算资源更少。