一个深度学习模型通过多源文献标准化机构名称特征融合:算法开发研究研究
Yifei Chen1, Xiaoying Li1, Aihua Li1
1Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.
JMIR formative research
|August 18, 2023
概括
一个新的深度学习模型准确地规范了机构名称,改善了文献检索和研究分析. 该系统识别变体,更新数据库,并达到93.79%的准确性,以更好地进行机构评估.
科学领域:
- 圣经计量和科学计量.
- 信息科学 信息科学
- 在研究中的人工智能.
背景情况:
- 机构名称的变化阻碍了准确的文献检索和学术成就的分析.
- 标准化机构名称对于评估研究竞争力至关重要.
- 深度学习提供先进的自然语言处理,以改善名称规范化.
研究的目的:
- 开发一个深度学习模型,使用合并的附属数据进行机构名称规范化.
- 通过权威文件实现高准确性标准化各种机构名称变体.
- 为了提高出版数据分析的可靠性.
主要方法:
- 利用了来自变压器的双向编码器表示 (BERT) 和其他深度学习模型.
- 纳入机构分类,层次关系提取和匹配/合并模型.
- 通过使用Dimensions,Web of Science和Scopus的数据进行预训练和微调训练模型.
主要成果:
- 该模型准确地识别并将标准机构名称与独特的ID联系起来.
- 它检测和更新权威文件中的非标准变体 (例如,缩写,复数).
- 在机构名称规范化方面取得了93.79%的准确率,包括处理未注册机构.
结论:
- 开发的深度学习模型在机构名称规范化方面表现出很高的准确性.
- 这种工具在评估机构竞争力和研究影响方面具有重大潜力.
- 应用包括分析机构研究领域和建立合作网络.
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