SubmissionNumber#=%=#12
FinalPaperTitle#=%=#Puer at SemEval-2024 Task 2: A BioLinkBERT Approach to Biomedical Natural Language Inference
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Jiaxu Dao
JobTitle#==#
Organization#==#
Abstract#==#This paper delineates our investigation into the application of BioLinkBERT for enhancing clinical trials, presented at SemEval-2024 Task 2. Centering on the medical biomedical NLI task, our approach utilized the BioLinkBERT-large model, refined with a pioneering mixed loss function that amalgamates contrastive learning and cross-entropy loss. This methodology demonstrably surpassed the established benchmark, securing an impressive F1 score of 0.72 and positioning our work prominently in the field. Additionally, we conducted a comparative analysis of various deep learning architectures, including BERT, ALBERT, and XLM-RoBERTa, within the context of medical text mining. The findings not only showcase our method's superior performance but also chart a course for future research in biomedical data processing. Our experiment source code is available on GitHub at: https://github.com/daojiaxu/semeval2024_task2.
Author{1}{Firstname}#=%=#Jiaxu
Author{1}{Lastname}#=%=#Dao
Author{1}{Email}#=%=#daojiaxu@peu.edu.cn
Author{1}{Affiliation}#=%=#Pu'er University
Author{2}{Firstname}#=%=#Zhuoying
Author{2}{Lastname}#=%=#Li
Author{2}{Email}#=%=#lizhuoying@peu.edu.cn
Author{2}{Affiliation}#=%=#Pu'er University
Author{3}{Firstname}#=%=#Xiuzhong
Author{3}{Lastname}#=%=#Tang
Author{3}{Email}#=%=#tangxiuzhong@peu.edu.cn
Author{3}{Affiliation}#=%=#Pu'er University
Author{4}{Firstname}#=%=#Xiaoli
Author{4}{Lastname}#=%=#Lan
Author{4}{Email}#=%=#lanxiaoli@peu.edu.cn
Author{4}{Affiliation}#=%=#Pu'er University
Author{5}{Firstname}#=%=#Junde
Author{5}{Lastname}#=%=#Wang
Author{5}{Email}#=%=#wangjunde@peu.edu.cn
Author{5}{Affiliation}#=%=#Pu'er University

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