Fine-Tuning BERTs for Definition Extraction from Mathematical Text
<|reference_start|>Fine-Tuning BERTs for Definition Extraction from Mathematical Text: 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.<|reference_end|>
@article{horowitz2024fine-tuning,
    title={Fine-Tuning BERTs for Definition Extraction from Mathematical Text},
    author={Lucy Horowitz and Ryan Hathaway},
    journal={arXiv preprint arXiv:2406.13827},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13827},
    primaryClass={cs.CL}
}