提高生物医学文献中的出版物类型和研究设计的自动索引,使用全文功能
Joe D Menke1, Shufan Ming1, Shruthan Radhakrishna2
1School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel Street, Champaign, 61820, IL, USA.
概括
这项研究通过使用全文功能和先进的BERT模型来增强生物医学文章索引,显著改善出版物类型和研究设计分类. 优化的SPECTER2基模型与特定的微调技术在识别物品特征方面取得了卓越的性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 信息检索 信息检索
背景情况:
- 对生物医学文章按出版物类型和研究设计进行准确的分类对于证据综合至关重要.
- 以前的方法依赖于有限的PubMed数据或特定的研究设计,如随机对照试验.
- 这项研究通过整合全文功能和高级文本表示来推进先前的工作.
研究的目的:
- 通过出版物类型和研究设计来提高生物医学文章索引的准确性.
- 评估不同基于BERT的编码器和微调策略的有效性.
- 利用全文内容和先进的优化技术来加强分类.
主要方法:
- 在PubMed文章 (1987-2023) 的数据集上微调各种基于BERT的编码器 (PubMedBERT,BioLinkBERT,SPECTER,SPECTER2).
- 纳入文本和口头化元数据,全文提取 (基于规则的,提取的,抽象的总结) 和专题信息.
- 采用了不对称损失,标签光滑和对比学习方法 (SimCSE, ADNCE, HeroCon, WeighCon) 进行优化.
- 评估模型使用精度,回忆,F1得分 (微和宏),以及ROC曲线下的面积 (AUC).
主要成果:
- 微调的SPECTER2基与"动物"MeSH术语,不对称损失,标签光滑和WeighCon对比损失显著超过了以前的架构.
- 不对称的损失和使用SPECTER2-base而不是PubMedBERT是提高性能的关键因素.
- 与基线相比,全文功能性能提高了2.4% (微F1) 和1.8% (宏F1).
结论:
- 全文功能,增强的文档表示和微调优化大大改善了出版物类型和研究设计索引.
- 未来的研究应该专注于改进标签准确性,提取相关信息,并扩大标签套件.
- 该研究为研究界提供了可访问的数据,代码和模型.
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