利用PubMed用户查询日志为推的类似文章提供后期解释
Ashley Shin1, James Anibal2, Qiao Jin1
1National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
ArXiv
|June 21, 2024
概括
研究人员现在可以获得类似文章推的解释. 一个新的模型,突出显示类似文章标题 (HSAT),使用PubMed数据来识别推标题中的关键短语,改进文献搜索.
科学领域:
- 生物医学信息学 生物医学信息学
- 信息检索 信息检索
- 人工智能的人工智能
背景情况:
- 像PubMed这样的学术搜索引擎提供"类似文章"功能来帮助研究人员.
- 当前的推系统往往缺乏解释,阻碍了文献搜索效率.
- 每年有数以百万计的生物医学论文被出版,这增加了对有效搜索工具的需求.
研究的目的:
- 开发一个模型,通过突出相关标题令牌来解释类似文章的建议.
- 为了利用用户查询日志进行培训和验证解释模型.
- 提高文献推系统的实用性和透明度.
主要方法:
- 从PubMed用户查询日志中重新定位了560万个共点击的文章对,以创建PubCLogs数据集.
- 训练了一个基于变压器的模型,突出显示类似文章标题 (HSAT),以根据种子文章的标题和摘要识别类似文章标题中的突出短语.
- 通过在标题中识别相关令牌,采用了后期解释方法.
主要成果:
- 在PubCLogs测试组中,HSAT获得了91.72%的F1得分,明显超过了BM25,MPNet,MedCPT,GPT-3.5和GPT-4等基线.
- 用手动注释数据集进行的独立评估证实了HSAT的强表现.
- 用户研究参与者更喜欢HSAT,因为它在解释中保持了简洁和全面的平衡.
结论:
- 重新利用学术搜索引擎用户查询日志是培养高级文献推解释模型的可行策略.
- 在解释类似文章建议方面,HSAT模型提供了显著的改进.
- 这种方法可以提高研究人员和临床医生的科学文献发现的效率和有效性.
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